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1. Search for events with a pair of displaced vertices from long-lived neutral particles decaying into hadronic jets in the ATLAS muon spectrometer in pp collisions at root s=13 TeV Aad, G. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_0_j_idt669",{id:"formSmash:items:resultList:0:j_idt669",widgetVar:"widget_formSmash_items_resultList_0_j_idt669",onLabel:"Aad, G. ",offLabel:"Aad, G. ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_0_j_idt672",{id:"formSmash:items:resultList:0:j_idt672",widgetVar:"widget_formSmash_items_resultList_0_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Aix Marseille Univ, CPPM, CNRS IN2P3, Marseille, France..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:0:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Leopold, AlexanderKTH, Skolan för teknikvetenskap (SCI), Fysik, Partikel- och astropartikelfysik.Lundberg, OlofKTH, Skolan för teknikvetenskap (SCI), Fysik, Partikel- och astropartikelfysik.Lund-Jensen, BengtKTH, Skolan för teknikvetenskap (SCI), Fysik, Partikel- och astropartikelfysik.Ohm, ChristianKTH, Skolan för teknikvetenskap (SCI), Fysik, Partikel- och astropartikelfysik.Ripellino, GiuliaKTH, Skolan för teknikvetenskap (SCI), Fysik, Partikel- och astropartikelfysik.Shaheen, RabiaKTH, Skolan för teknikvetenskap (SCI), Fysik, Partikel- och astropartikelfysik.Shope, David R.KTH, Skolan för teknikvetenskap (SCI), Fysik, Partikel- och astropartikelfysik.Strandberg, JonasKTH, Skolan för teknikvetenskap (SCI), Fysik, Partikel- och astropartikelfysik.Zwalinski, L.CERN, Geneva, Switzerland..et al.,PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:0:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Search for events with a pair of displaced vertices from long-lived neutral particles decaying into hadronic jets in the ATLAS muon spectrometer in pp collisions at root s=13 TeV2022Inngår i: Physical Review D: covering particles, fields, gravitation, and cosmology, ISSN 2470-0010, E-ISSN 2470-0029, Vol. 106, nr 3, artikkel-id 032005Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_0_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:0:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_0_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); A search for events with two displaced vertices from long-lived particle (LLP) pairs using data collected by the ATLAS detector at the LHC is presented. This analysis uses 139 fb(-1) of proton-proton collision data at root s=13 TeV recorded in 2015-2018. The search employs techniques for reconstructing vertices of LLPs decaying to jets in the muon spectrometer displaced between 3 and 14 m with respect to the primary interaction vertex. The observed numbers of events are consistent with the expected background and limits for several benchmark signals are determined. For the Higgs boson with a mass of 125 GeV, the paper reports the first exclusion limits for branching fractions into neutral long-lived particles below 0.1%, while branching fractions above 10% are excluded at 95% confidence level for LLP proper lifetimes ranging from 4 cm to 72.4 m. In addition, the paper present the first results for the decay of LLPs into (tt) over bar in the ATLAS muon spectrometer.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:0:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 2. Limiting directions for random walks in classical affine Weyl groups Aas, Eriket al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt672",{id:"formSmash:items:resultList:1:j_idt672",widgetVar:"widget_formSmash_items_resultList_1_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:1:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Ayyer, ArvindLinusson, SvanteKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).Potka, SamuKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:1:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Limiting directions for random walks in classical affine Weyl groupsManuskript (preprint) (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:1:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_1_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Let be a finite Weyl group and the corresponding affine Weyl group. A random element of can be obtained as a reduced random walk on the alcoves of . By a theorem of Lam (Ann. Probab. 2015), such a walk almost surely approaches one of many directions. We compute these directions when is , and and the random walk is weighted by Kac and dual Kac labels. This settles Lam's questions for types and in the affirmative and for type in the negative. The main tool is a combinatorial two row model for a totally asymmetric simple exclusion process called the -TASEP, with four parameters. By specializing the parameters in different ways, we obtain TASEPs for each of the Weyl groups mentioned above. Computing certain correlations in these TASEPs gives the desired limiting directions.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:1:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_1_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:1:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_1_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:1:j_idt946:0:fullText"});}); 3. Updated relations for the uniaxial compressive strength of marlstones based on P-wave velocity and point load index test Abbaszadeh Shahri, Abbas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_2_j_idt669",{id:"formSmash:items:resultList:2:j_idt669",widgetVar:"widget_formSmash_items_resultList_2_j_idt669",onLabel:"Abbaszadeh Shahri, Abbas ",offLabel:"Abbaszadeh Shahri, Abbas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_2_j_idt672",{id:"formSmash:items:resultList:2:j_idt672",widgetVar:"widget_formSmash_items_resultList_2_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:2:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Larsson, StefanKTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Jord- och bergmekanik.Johansson, FredrikKTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Jord- och bergmekanik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:2:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Updated relations for the uniaxial compressive strength of marlstones based on P-wave velocity and point load index test2016Inngår i: INNOVATIVE INFRASTRUCTURE SOLUTIONS, ISSN 2364-4176, Vol. 1, nr 1, artikkel-id UNSP 17Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_2_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:2:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_2_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Although there are many proposed relations for different rock types to predict the uniaxial compressive strength (UCS) as a function of P-wave velocity (V-P) and point load index (Is), only a few of them are focused on marlstones. However, these studies have limitations in applicability since they are mainly based on local studies. In this paper, an attempt is therefore made to present updated relations for two previous proposed correlations for marlstones in Iran. The modification process is executed through multivariate regression analysis techniques using a provided comprehensive database for marlstones in Iran, including UCS, V-P and Is from publications and validated relevant sources comprising 119 datasets. The accuracy, appropriateness and applicability of the obtained modifications were tested by means of different statistical criteria and graph analyses. The conducted comparison between updated and previous proposed relations highlighted better applicability in the prediction of UCS using the updated correlations introduced in this study. However, the derived updated predictive models are dependent on rock types and test conditions, as they are in this study.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:2:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 4. Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors Abdalmoaty, Mohamed PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_3_j_idt669",{id:"formSmash:items:resultList:3:j_idt669",widgetVar:"widget_formSmash_items_resultList_3_j_idt669",onLabel:"Abdalmoaty, Mohamed ",offLabel:"Abdalmoaty, Mohamed ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:3:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:3:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors2017Licentiatavhandling, monografi (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_3_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:3:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_3_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. Albeit asymptotically optimal, these methods come with several computational challenges and fundamental limitations.

The contributions of this thesis can be divided into two main parts. In the first part, approximate solutions to the maximum likelihood problem are explored. Both analytical and numerical approaches, based on the expectation-maximization algorithm and the quasi-Newton algorithm, are considered. While analytic approximations are difficult to analyze, asymptotic guarantees can be established for methods based on Monte Carlo approximations. Yet, Monte Carlo methods come with their own computational difficulties; sampling in high-dimensional spaces requires an efficient proposal distribution to reduce the number of required samples to a reasonable value.

In the second part, relatively simple prediction error method estimators are proposed. They are based on non-stationary one-step ahead predictors which are linear in the observed outputs, but are nonlinear in the (assumed known) input. These predictors rely only on the first two moments of the model and the computation of the likelihood function is not required. Consequently, the resulting estimators are defined via analytically tractable objective functions in several relevant cases. It is shown that, under mild assumptions, the estimators are consistent and asymptotically normal. In cases where the first two moments are analytically intractable due to the complexity of the model, it is possible to resort to vanilla Monte Carlo approximations. Several numerical examples demonstrate a good performance of the suggested estimators in several cases that are usually considered challenging.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:3:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_3_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:3:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_3_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:3:j_idt946:0:fullText"});}); 5. Predicting Customer Conversion using Supervised Machine Learning Aboud, Stephanie PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt669",{id:"formSmash:items:resultList:4:j_idt669",widgetVar:"widget_formSmash_items_resultList_4_j_idt669",onLabel:"Aboud, Stephanie ",offLabel:"Aboud, Stephanie ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:4:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:4:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Predicting Customer Conversion using Supervised Machine Learning2021Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:4:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_4_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The growth of e-commerce has been evident over the past years and for companies like Klarna that provides payment solutions, focusing on the purchase experience is more important than ever. With that goal in mind, more companies are using machine learning methods and tools to make predictions and forecast future outcomes, giving them a competitive advantage on the market. This thesis aims to apply supervised machine learning techniques to predict customer conversion, i.e. predict if a customer with a started shopping session will complete the purchase. The purpose of the project is to also determine which supervised learning algorithm performs the best when predicting customer conversion, with regards to a set of model evaluation metrics. The classical classification method Logistic Regression was tested, as well as the machine learning methods Support vector Machine, Random forest and XGBoost. The metrics used to evaluate the model performances were Precision, Recall, F1- and AUC-scores. Furthermore, the SHapley Additive exPlanations approach was implemented for feature importance and for interpreting tree-based models. The results showed that it is in fact possible to predict customer conversion using machine learning. All models yielded good performance and the difference in performance was relatively small. XGBoost performed slightly better than the rest of the models.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:4:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_4_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:4:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_4_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:4:j_idt946:0:fullText"});}); 6. Sustainable Investments - The impact of the EU green taxonomy Abrahamsson, Ville PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt669",{id:"formSmash:items:resultList:5:j_idt669",widgetVar:"widget_formSmash_items_resultList_5_j_idt669",onLabel:"Abrahamsson, Ville ",offLabel:"Abrahamsson, Ville ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt672",{id:"formSmash:items:resultList:5:j_idt672",widgetVar:"widget_formSmash_items_resultList_5_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:5:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Ekblom, JuliaKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:5:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Sustainable Investments - The impact of the EU green taxonomy2021Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:5:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_5_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The increasing environmental issues and the measures taken to tackle them, is a topic of high significance in today's society. In light of this, the EU is underway with developing a taxonomy classifying sustainable economic activities in hopes to raise awareness, increase transparency regarding environmental impact, and motivate investors to invest sustainable. This paper aims to examine if the taxonomy is relevant to its cause, as well as if sustainability factors can be identified with linear regression connected to growth in a company's value, which may motivate sustainable investments. Several interviews were conducted, along with the creation of a mathematical model. The conclusions drawn was that it is not viable to determine a company's growth in value using solely sustainability factors. However, the results were promising regarding the implementation of sustainability factors in more comprehensive models. Furthermore, the impact of the taxonomy was hard to predict at this time, however, the consensus of the majority of the interviews conducted with experts on the subject, is that it has potential to impact sustainable investments in the future. Future research on the taxonomy may yield results of higher interest since more comprehensive data will be available, and the impact of the taxonomy will be more concrete.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:5:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_5_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:5:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_5_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:5:j_idt946:0:fullText"});}); 7. Banach Wasserstein GAN Adler, Jonas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt669",{id:"formSmash:items:resultList:6:j_idt669",widgetVar:"widget_formSmash_items_resultList_6_j_idt669",onLabel:"Adler, Jonas ",offLabel:"Adler, Jonas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt672",{id:"formSmash:items:resultList:6:j_idt672",widgetVar:"widget_formSmash_items_resultList_6_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:6:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Lunz, SebastianUniv Cambridge, Dept Appl Math & Theoret Phys, Cambridge, England..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:6:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Banach Wasserstein GAN2018Inngår i: Advances in Neural Information Processing Systems 31 (NIPS 2018) / [ed] Bengio, S Wallach, H Larochelle, H Grauman, K CesaBianchi, N Garnett, R, Neural Information Processing Systems (NIPS) , 2018Konferansepaper (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:6:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_6_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Wasserstein Generative Adversarial Networks (WGANs) can be used to generate realistic samples from complicated image distributions. The Wasserstein metric used in WGANs is based on a notion of distance between individual images, which induces a notion of distance between probability distributions of images. So far the community has considered l(2) as the underlying distance. We generalize the theory of WGAN with gradient penalty to Banach spaces, allowing practitioners to select the features to emphasize in the generator. We further discuss the effect of some particular choices of underlying norms, focusing on Sobolev norms. Finally, we demonstrate a boost in performance for an appropriate choice of norm on CIFAR-10 and CelebA.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:6:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 8. Learning to solve inverse problems using Wasserstein loss Adler, Jonas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt669",{id:"formSmash:items:resultList:7:j_idt669",widgetVar:"widget_formSmash_items_resultList_7_j_idt669",onLabel:"Adler, Jonas ",offLabel:"Adler, Jonas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt672",{id:"formSmash:items:resultList:7:j_idt672",widgetVar:"widget_formSmash_items_resultList_7_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.). Elekta, Box 7593, 103 93 Stockholm, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:7:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Ringh, AxelKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.Öktem, OzanKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).Karlsson, JohanKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:7:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Learning to solve inverse problems using Wasserstein lossManuskript (preprint) (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:7:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_7_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We propose using the Wasserstein loss for training in inverse problems. In particular, we consider a learned primal-dual reconstruction scheme for ill-posed inverse problems using the Wasserstein distance as loss function in the learning. This is motivated by miss-alignments in training data, which when using standard mean squared error loss could severely degrade reconstruction quality. We prove that training with the Wasserstein loss gives a reconstruction operator that correctly compensates for miss-alignments in certain cases, whereas training with the mean squared error gives a smeared reconstruction. Moreover, we demonstrate these effects by training a reconstruction algorithm using both mean squared error and optimal transport loss for a problem in computerized tomography.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:7:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 9. Block-approximated exponential random graphs Adriaens, Florian PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt669",{id:"formSmash:items:resultList:8:j_idt669",widgetVar:"widget_formSmash_items_resultList_8_j_idt669",onLabel:"Adriaens, Florian ",offLabel:"Adriaens, Florian ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt672",{id:"formSmash:items:resultList:8:j_idt672",widgetVar:"widget_formSmash_items_resultList_8_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Teoretisk datalogi, TCS.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:8:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Mara, AlexandruIDLab, Ghent University, Belgium.Lijffijt, JefreyIDLab, Ghent University, Belgium.De Bie, TijlIDLab, Ghent University, Belgium.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:8:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Block-approximated exponential random graphs2020Inngår i: Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020, Institute of Electrical and Electronics Engineers Inc. , 2020, s. 70-80Konferansepaper (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:8:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_8_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs. By utilizing fast matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions, while being able to meaningfully model local information of the graph (e.g., degrees) as well as global information (e.g., clustering coefficient, assortativity, etc.) if desired. This allows one to efficiently generate random networks with similar properties as an observed network, and the models can be used for several downstream tasks such as link prediction. Our methods are scalable to sparse graphs consisting of millions of nodes.Empirical evaluation demonstrates competitiveness in terms of both speed and accuracy with state-of-the-art methods - which are typically based on embedding the graph into some low-dimensional space - for link prediction, showcasing the potential of a more direct and interpretable probablistic model for this task.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:8:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 10. Modeling and Optimization of the Early Baggage Storage at Stockholm Arlanda Airport Terminal 5 Ageling, Lisette PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt669",{id:"formSmash:items:resultList:9:j_idt669",widgetVar:"widget_formSmash_items_resultList_9_j_idt669",onLabel:"Ageling, Lisette ",offLabel:"Ageling, Lisette ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt672",{id:"formSmash:items:resultList:9:j_idt672",widgetVar:"widget_formSmash_items_resultList_9_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:9:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Alm, TureKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:9:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Modeling and Optimization of the Early Baggage Storage at Stockholm Arlanda Airport Terminal 52022Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:9:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_9_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This report was written in cooperation with Swedavia and aimed to examine and optimize the time before flight departure the chutes should open. Chutes are the last state before the baggage is transported to the plane. Currently, the chutes open two hours before departure. If baggage arrives while its corresponding chute is closed it will go to the Early Baggage System (EBS) where it will stay until two hours before departure. Afterward, the baggage will be transported to its corresponding chute. By reducing the time in the chute, the airport would utilize the EBS more efficiently and make the chutes available to more planes.

To examine the resilience of the system it was modeled and simulated in Matlab. The parameters that were used to simulate the model were taken from a data set provided by Swedavia. The conclusion is that there's a possibility for higher utilization of the EBS by lowering the allotted times of chutes per flight and thereby freeing capacity in the makeup sector. This should be further investigated to see the effects of additional processes in the baggage handling system.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:9:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_9_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:9:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_9_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:9:j_idt946:0:fullText"});}); 11. Statistical Learning and Analysis on Homology-Based Features Agerberg, Jens PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt669",{id:"formSmash:items:resultList:10:j_idt669",widgetVar:"widget_formSmash_items_resultList_10_j_idt669",onLabel:"Agerberg, Jens ",offLabel:"Agerberg, Jens ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:10:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:10:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Statistical Learning and Analysis on Homology-Based Features2020Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:10:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_10_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stable rank has recently been proposed as an invariant to encode the result of persistent homology, a method used in topological data analysis. In this thesis we develop methods for statistical analysis as well as machine learning methods based on stable rank. As stable rank may be viewed as a mapping to a Hilbert space, a kernel can be constructed from the inner product in this space. First, we investigate this kernel in the context of kernel learning methods such as support-vector machines. Next, using the theory of kernel embedding of probability distributions, we give a statistical treatment of the kernel by showing some of its properties and develop a two-sample hypothesis test based on the kernel. As an alternative approach, a mapping to a Euclidean space with learnable parameters can be conceived, serving as an input layer to a neural network. The developed methods are first evaluated on synthetic data. Then the two-sample hypothesis test is applied on the OASIS open access brain imaging dataset. Finally a graph classification task is performed on a dataset collected from Reddit.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:10:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_10_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:10:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_10_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:10:j_idt946:0:fullText"});}); 12. True risk of illiquid investments Agering, Harald PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_11_j_idt669",{id:"formSmash:items:resultList:11:j_idt669",widgetVar:"widget_formSmash_items_resultList_11_j_idt669",onLabel:"Agering, Harald ",offLabel:"Agering, Harald ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:11:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:11:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); True risk of illiquid investments2018Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_11_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:11:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_11_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Alternative assets are becoming a considerable portion of global financial markets. Some of these alternative assets are highly illiquid, and as such they may require more intricate methods for calculating risk and performance statistics accurately. Research on hedge funds has established a pattern of risk being understated and various measures of performance being overstated due to illiquidity of the assets. This paper sets out to prove the existence of such bias and presents methods for removing it. Four mathematical methods aiming to adjust statistics for sparse return series were considered, and an implementation was carried out for data on private equity, real estate and infrastructure assets. The results indicate that there are in general substantial adjustments made to the risk and performance statistics of the illiquid assets when using these methods. In particular, the volatility and market exposure were adjusted upwards while manager skill and risk-adjusted performance were adjusted downwards.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:11:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_11_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:11:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_11_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:11:j_idt946:0:fullText"});}); 13. On a class of reflected backward stochastic Volterra integral equations and related time-inconsistent optimal stopping problems Agram, Nacira PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt669",{id:"formSmash:items:resultList:12:j_idt669",widgetVar:"widget_formSmash_items_resultList_12_j_idt669",onLabel:"Agram, Nacira ",offLabel:"Agram, Nacira ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt672",{id:"formSmash:items:resultList:12:j_idt672",widgetVar:"widget_formSmash_items_resultList_12_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Linnaeus Univ LNU, Dept Math, Växjö, Sweden..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:12:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Djehiche, BoualemKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:12:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); On a class of reflected backward stochastic Volterra integral equations and related time-inconsistent optimal stopping problems2021Inngår i: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 155, s. 104989-, artikkel-id 104989Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:12:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_12_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We introduce a class of one-dimensional continuous reflected backward stochastic Volterra integral equations driven by Brownian motion, where the reflection keeps the solution above a given stochastic process (lower obstacle). We prove existence and uniqueness by a fixed point argument and derive a comparison result. Moreover, we show how the solution of our problem is related to a time-inconsistent optimal stopping problem and derive an optimal strategy.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:12:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 14. Mean-field backward stochastic differential equations and applications Agram, Nacira PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_13_j_idt669",{id:"formSmash:items:resultList:13:j_idt669",widgetVar:"widget_formSmash_items_resultList_13_j_idt669",onLabel:"Agram, Nacira ",offLabel:"Agram, Nacira ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_13_j_idt672",{id:"formSmash:items:resultList:13:j_idt672",widgetVar:"widget_formSmash_items_resultList_13_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:13:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hu, YaozhongUniv Alberta, Dept Math & Stat Sci, Edmonton, AB T6G 2G1, Canada..oksendal, BerntUniv Oslo, Dept Math, POB 1053, N-0316 Oslo, Norway..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:13:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Mean-field backward stochastic differential equations and applications2022Inngår i: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 162, artikkel-id 105196Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_13_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:13:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_13_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In this paper we study the linear mean-field backward stochastic differential equations (mean-field BSDE) of the form & nbsp;& nbsp;{dY(t) = -[alpha(1)(t)Y(t) +& nbsp;beta(1)(t)Z(t) +& nbsp;integral(R0 & nbsp;)eta(1)(t,& nbsp;zeta)K(t,& nbsp;zeta)nu(d zeta) +& nbsp;alpha(2)(t)E[Y(t)] +& nbsp;beta(2)(t)E[Z(t)] +& nbsp;integral(R0 & nbsp;)eta(2)(t,& nbsp;zeta)E[K(t,& nbsp;zeta)]nu(d zeta) +& nbsp;gamma(t)]dt + Z(t)dB(t) +& nbsp;integral K-R0 (t,& nbsp;zeta)(N) over tilde(dt, d zeta), t & nbsp;is an element of & nbsp;[0, T].Y(T) =xi.& nbsp;& nbsp;where (Y, Z, K) is the unknown solution triplet, B is a Brownian motion, (N) over tilde is a compensated Poisson random measure, independent of B. We prove the existence and uniqueness of the solution triplet (Y, Z, K) of such systems. Then we give an explicit formula for the first component Y(t) by using partial Malliavin derivatives. To illustrate our result we apply them to study a mean-field recursive utility optimization problem in finance.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:13:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 15. Stochastic Fokker-Planck equations for conditional McKean-Vlasov jump diffusions and applications to optimal control Agram, Nacira PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_14_j_idt669",{id:"formSmash:items:resultList:14:j_idt669",widgetVar:"widget_formSmash_items_resultList_14_j_idt669",onLabel:"Agram, Nacira ",offLabel:"Agram, Nacira ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_14_j_idt672",{id:"formSmash:items:resultList:14:j_idt672",widgetVar:"widget_formSmash_items_resultList_14_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:14:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Øksendal, BerntDepartment of Mathematics, University of Oslo, Oslo, Norway.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:14:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Stochastic Fokker-Planck equations for conditional McKean-Vlasov jump diffusions and applications to optimal control2023Inngår i: SIAM Journal of Control and Optimization, ISSN 0363-0129, E-ISSN 1095-7138, Vol. 61, nr 3, s. 1472-1493Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_14_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:14:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_14_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The purpose of this paper is to study optimal control of conditional McKean-Vlasov (mean-field) stochastic differential equations with jumps (conditional McKean-Vlasov jump diffu-sions, for short). To this end, we first prove a stochastic Fokker-Planck equation for the conditional law of the solution of such equations. Combining this equation with the original state equation, we obtain a Markovian system for the state and its conditional law. Furthermore, we apply this to formulate a Hamilton-Jacobi-Bellman equation for the optimal control of conditional McKean-Vlasov jump diffusions. Then we study the situation when the law is absolutely continuous with respect to Lebesgue measure. In that case the Fokker-Planck equation reduces to a stochastic par-tial differential equation for the Radon-Nikodym derivative of the conditional law. Finally we apply these results to solve explicitly the linear-quadratic optimal control problem of conditional stochastic McKean-Vlasov jump diffusions, and optimal consumption from a cash flow.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:14:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 16. Application of the Ordered Lorenz Curve in the Analysis of a Non-Life Insurance Portfolio Ahlberg, Fredrik PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_15_j_idt669",{id:"formSmash:items:resultList:15:j_idt669",widgetVar:"widget_formSmash_items_resultList_15_j_idt669",onLabel:"Ahlberg, Fredrik ",offLabel:"Ahlberg, Fredrik ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:15:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:15:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Application of the Ordered Lorenz Curve in the Analysis of a Non-Life Insurance Portfolio2019Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_15_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:15:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_15_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Insurance analysts have a great variety of assessment tools at their disposal in order to ensure a healthy insurance portfolio. To describe the financial income and loss distribution of the insurance portfolio one of the more fundamental mathematical instrument is the Lorenz curve. A measure developed in the early 19th centrury by Max O. Lorenz which intended to describe a population’s income distribution in a macro perspective. By developing further on this method with guidance from the article by Frees, Meyers and Cummings, [5], a link between the Lorenz curve and the insurance portfolio’s risk segment will be investigated.

By constructing an insurance rating function which determine an insurance expected loss, depending on the policyholders characteristics, ordering the premium and loss distributions by its relative loss the intent is to identify profitable blocks along the ordered Lorenz curve. With this insight an analyst can redefine the portfolio structure and highlight the desirable characteristics which define a policyholder. In order to keep up with the competition an insurer has to, in the long run, create a sustainable, profitable portfolio with lowering the risk of occurring greater insurance claims.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:15:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_15_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:15:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_15_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:15:j_idt946:0:fullText"});}); 17. Claims Reserving using Gradient Boosting and Generalized Linear Models Ahlgren, Marcus PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_16_j_idt669",{id:"formSmash:items:resultList:16:j_idt669",widgetVar:"widget_formSmash_items_resultList_16_j_idt669",onLabel:"Ahlgren, Marcus ",offLabel:"Ahlgren, Marcus ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:16:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:16:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Claims Reserving using Gradient Boosting and Generalized Linear Models2018Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_16_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:16:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_16_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); One fundamental function of an insurance company revolves around calculating the expected claims costs for which the insurer has to compensate its policyholders for. This is the process of claims reserving which is practised by actuaries using statistical methods. Over the last few decades statistical learning methods have become increasingly popular due to their ability to find complex patterns in any type of data. However, they have not been widely adapted within the insurance sector. In this thesis we evaluate the capability of claims reserving with the method of gradient boosting, a non-parametric statistical learning method that has proven to be successful within multiple other disciplines which has made it very popular. The gradient boosting technique is compared with the generalized linear model(GLM) which is widely used for modelling claims. We compare the models by using a claims data set provided by Länsförsäkringar AB which allows us to train the models and evaluate their performance on data not yet seen by the models. The models were implemented using R. The results show that the GLM has a lower prediction error. Also, the gradient boosting method requires more fine tuning to handle claims data properly while the GLM already possesses certain features that makes it suitable for claims reserving without making as many adjustments in the model implementation. The advantage of capturing complex dependencies in data is not fully utilized in this thesis since we only work with 6 predictor variables. It is more likely that gradient boosting can compete with GLM when predicting more complicated claims.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:16:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_16_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:16:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_16_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:16:j_idt946:0:fullText"});}); 18. Internal Market Risk Modelling for Power Trading Companies Ahlgren, Markus PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_17_j_idt669",{id:"formSmash:items:resultList:17:j_idt669",widgetVar:"widget_formSmash_items_resultList_17_j_idt669",onLabel:"Ahlgren, Markus ",offLabel:"Ahlgren, Markus ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:17:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:17:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Internal Market Risk Modelling for Power Trading Companies2015Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_17_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:17:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_17_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Since the financial crisis of 2008, the risk awareness has increased in the -financial sector. Companies are regulated with regards to risk exposure. These regulations are driven by the Basel Committee that formulates broad supervisory standards, guidelines and recommends statements of best practice in banking supervision. In these regulations companies are regulated with own funds requirements for market risks.

This thesis constructs an internal model for risk management that, according to the "Capital Requirements Regulation" (CRR) respectively the "Fundamental Review of the Trading Book" (FRTB), computes the regulatory capital requirements for market risks. The capital requirements according to CRR and FRTB are compared to show how the suggested move to an expected shortfall (

*ES*) based model in FRTB will affect the capital requirements. All computations are performed with data that have been provided from a power trading company to make the results fit reality. In the results, when comparing the risk capital requirements according to CRR and FRTB for a power portfolio with only linear assets, it shows that the risk capital is higher using the value-at-risk (*VaR*) based model. This study shows that the changes in risk capital mainly depend on the different methods of calculating the risk capital according to CRR and FRTB respectively and minor on the change of risk measure.PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:17:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_17_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:17:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_17_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:17:j_idt946:0:fullText"});}); 19. Importance Sampling for Least-Square Monte Carlo Methods Ahmed, Ilyas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_18_j_idt669",{id:"formSmash:items:resultList:18:j_idt669",widgetVar:"widget_formSmash_items_resultList_18_j_idt669",onLabel:"Ahmed, Ilyas ",offLabel:"Ahmed, Ilyas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:18:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:18:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Importance Sampling for Least-Square Monte Carlo Methods2016Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_18_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:18:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_18_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Pricing American style options is challenging due to early exercise opportunities. The conditional expectation in the Snell envelope, known as the continuation value is approximated by basis functions in the Least-Square Monte Carlo-algorithm, giving robust estimation for the options price. By change of measure in the underlying Geometric Brownain motion using Importance Sampling, the variance of the option price can be reduced up to 9 times. Finding the optimal estimator that gives the minimal variance requires careful consideration on the reference price without adding bias in the estimator. A stochastic algorithm is used to find the optimal drift that minimizes the second moment in the expression of the variance after change of measure. The usage of Importance Sampling shows significant variance reduction in comparison with the standard Least-Square Monte Carlo. However, Importance Sampling method may be a better alternative for more complex instruments with early exercise opportunity.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:18:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_18_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:18:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_18_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:18:j_idt946:0:fullText"});}); 20. Distributed Largest Eigenvalue-Based Spectrum Sensing Using Diffusion LMS Ainomae, Ahti PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_19_j_idt669",{id:"formSmash:items:resultList:19:j_idt669",widgetVar:"widget_formSmash_items_resultList_19_j_idt669",onLabel:"Ainomae, Ahti ",offLabel:"Ainomae, Ahti ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_19_j_idt672",{id:"formSmash:items:resultList:19:j_idt672",widgetVar:"widget_formSmash_items_resultList_19_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Teknisk informationsvetenskap.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:19:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Bengtsson, MatsKTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Teknisk informationsvetenskap.Trump, TonuTallinn Univ Technol, Dept Radio & Telecommun Engn, EE-12616 Tallinn, Estonia..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:19:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Distributed Largest Eigenvalue-Based Spectrum Sensing Using Diffusion LMS2018Inngår i: IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, ISSN 2373-776X, Vol. 4, nr 2, s. 362-377Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_19_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:19:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_19_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In this paper, we propose a distributed detection scheme for cognitive radio (CR) networks, based on the largest eigenvalues (LEs) of adaptively estimated correlation matrices (CMs), assuming that the primary user signal is temporally correlated. The proposed algorithm is fully distributed, there by avoiding the potential single point of failure that a fusion center would imply. Different forms of diffusion least mean square algorithms are used for estimating and averaging the CMs over the CR network for the LE detection and the resulting estimation performance is analyzed using a common framework. In order to obtain analytic results on the detection performance, the exact distribution of the CM estimates are approximated by a Wishart distribution, by matching the moments. The theoretical findings are verified through simulations.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:19:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 21. Profitability of Technical Trading Strategies in the Swedish Equity Market Alam, Azmain PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt669",{id:"formSmash:items:resultList:20:j_idt669",widgetVar:"widget_formSmash_items_resultList_20_j_idt669",onLabel:"Alam, Azmain ",offLabel:"Alam, Azmain ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt672",{id:"formSmash:items:resultList:20:j_idt672",widgetVar:"widget_formSmash_items_resultList_20_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:20:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Norrström, GustavKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:20:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Profitability of Technical Trading Strategies in the Swedish Equity Market2021Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:20:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_20_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This study aims to see if it is possible to generate abnormal returns in the Swedishstock market through the use of three different trading strategies based on technicalindicators. As the indicators are based on historical price data only, the study assumesweak market efficiency according to the efficient market hypothesis. The study isconducted using daily prices for OMX Stockholm PI and STOXX 600 Europe from theperiod between 1 January 2010 and 31 December 2019. Trading positions has beentaken in the OMX Stockholm PI index while STOXX 600 Europe has been used torepresent the market portfolio. Abnormal returns has been defined as the Jensen’s αin a Fama French three factor model with Carhart extension. This period has beencharacterised by increasing prices (a bull market) which may have had an impact onthe results. Furthermore, a higher frequency of rebalancing for the Fama French andCarhart model could also increase the quality of the results. The results indicate thatall three strategies has generated abnormal returns during the period.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:20:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_20_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:20:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_20_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:20:j_idt946:0:fullText"});}); 22. Robust model training and generalisation with Studentising flows Alexanderson, Simon PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt669",{id:"formSmash:items:resultList:21:j_idt669",widgetVar:"widget_formSmash_items_resultList_21_j_idt669",onLabel:"Alexanderson, Simon ",offLabel:"Alexanderson, Simon ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt672",{id:"formSmash:items:resultList:21:j_idt672",widgetVar:"widget_formSmash_items_resultList_21_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:21:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Henter, Gustav EjeKTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:21:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Robust model training and generalisation with Studentising flows2020Inngår i: Proceedings of the ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models / [ed] Chin-Wei Huang, David Krueger, Rianne van den Berg, George Papamakarios, Chris Cremer, Ricky Chen, Danilo Rezende, 2020, Vol. 2, s. 25:1-25:9, artikkel-id 25Konferansepaper (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:21:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_21_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Normalising flows are tractable probabilistic models that leverage the power of deep learning to describe a wide parametric family of distributions, all while remaining trainable using maximum likelihood. We discuss how these methods can be further improved based on insights from robust (in particular, resistant) statistics. Specifically, we propose to endow flow-based models with fat-tailed latent distributions such as multivariate Student's t, as a simple drop-in replacement for the Gaussian distribution used by conventional normalising flows. While robustness brings many advantages, this paper explores two of them: 1) We describe how using fatter-tailed base distributions can give benefits similar to gradient clipping, but without compromising the asymptotic consistency of the method. 2) We also discuss how robust ideas lead to models with reduced generalisation gap and improved held-out data likelihood. Experiments on several different datasets confirm the efficacy of the proposed approach in both regards.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:21:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)alexanderson2020robust$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_21_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:21:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_21_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:21:j_idt946:0:fullText"});}); 23. Statistical Analysis of Computer Network Security Ali, Dana PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt669",{id:"formSmash:items:resultList:22:j_idt669",widgetVar:"widget_formSmash_items_resultList_22_j_idt669",onLabel:"Ali, Dana ",offLabel:"Ali, Dana ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt672",{id:"formSmash:items:resultList:22:j_idt672",widgetVar:"widget_formSmash_items_resultList_22_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:22:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Kap, GoranKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:22:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Statistical Analysis of Computer Network Security2013Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:22:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_22_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In this thesis it isshown how to measure the annual loss expectancy of computer networks due to therisk of cyber attacks. With the development of metrics for measuring theexploitation difficulty of identified software vulnerabilities, it is possibleto make a measurement of the annual loss expectancy for computer networks usingBayesian networks. To enable the computations, computer net-work vulnerabilitydata in the form of vulnerability model descriptions, vulnerable dataconnectivity relations and intrusion detection system measurements aretransformed into vector based numerical form. This data is then used to generatea probabilistic attack graph which is a Bayesian network of an attack graph.The probabilistic attack graph forms the basis for computing the annualizedloss expectancy of a computer network. Further, it is shown how to compute anoptimized order of vulnerability patching to mitigate the annual lossexpectancy. An example of computation of the annual loss expectancy is providedfor a small invented example network

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:22:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_22_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:22:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_22_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:22:j_idt946:0:fullText"});}); 24. Improved Battery Cycle Life Prediction Using a Hybrid Data-Driven Model Incorporating Linear Support Vector Regression and Gaussian Alipour, M.et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_23_j_idt672",{id:"formSmash:items:resultList:23:j_idt672",widgetVar:"widget_formSmash_items_resultList_23_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:23:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Tavallaey, Shiva SanderKTH, Skolan för teknikvetenskap (SCI), Teknisk mekanik, Farkostteknik och Solidmekanik. ABB AB Corporate Research, Forskargränd 7, SE-721 78 Västerås, Sweden.Andersson, A. M.Brandell, D.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:23:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Improved Battery Cycle Life Prediction Using a Hybrid Data-Driven Model Incorporating Linear Support Vector Regression and Gaussian2022Inngår i: ChemPhysChem, ISSN 1439-4235, E-ISSN 1439-7641, Vol. 23, nr 7, artikkel-id e202100829Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_23_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:23:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_23_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The ability to accurately predict lithium-ion battery life-time already at an early stage of battery usage is critical for ensuring safe operation, accelerating technology development, and enabling battery second-life applications. Many models are unable to effectively predict battery life-time at early cycles due to the complex and nonlinear degrading behavior of lithium-ion batteries. In this study, two hybrid data-driven models, incorporating a traditional linear support vector regression (LSVR) and a Gaussian process regression (GPR), were developed to estimate battery life-time at an early stage, before more severe capacity fading, utilizing a data set of 124 battery cells with lifetimes ranging from 150 to 2300 cycles. Two type of hybrid models, here denoted as A and B, were proposed. For each of the models, we achieved 1.1 % (A) and 1.4 % (B) training error, and similarly, 8.3 % (A) and 8.2 % (B) test error. The two key advantages are that the error percentage is kept below 10 % and that very low error values for the training and test sets were observed when utilizing data from only the first 100 cycles.The proposed method thus appears highly promising for predicting battery life during early cycles.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:23:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 25. Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net Allerbo, Oskar PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_24_j_idt669",{id:"formSmash:items:resultList:24:j_idt669",widgetVar:"widget_formSmash_items_resultList_24_j_idt669",onLabel:"Allerbo, Oskar ",offLabel:"Allerbo, Oskar ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:24:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Jörnsten, RebeckaKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:24:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net2023Inngår i: Journal of machine learning research, ISSN 1532-4435, E-ISSN 1533-7928, Vol. 24, nr 277, s. 1-53Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_24_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:24:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_24_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The elastic net combines lasso and ridge regression to fuse the sparsity property of lasso with the grouping property of ridge regression. The connections between ridge regression and gradient descent and between lasso and forward stagewise regression have previously been shown. Similar to how the elastic net generalizes lasso and ridge regression, we introduce elastic gradient descent, a generalization of gradient descent and forward stagewise regression. We theoretically analyze elastic gradient descent and compare it to the elastic net and forward stagewise regression. Parts of the analysis are based on elastic gradient flow, a piecewise analytical construction, obtained for elastic gradient descent with infinitesimal step size. We also compare elastic gradient descent to the elastic net on real and simulated data and show that it provides similar solution paths, but is several orders of magnitude faster. Compared to forward stagewise regression, elastic gradient descent selects a model that, although still sparse, provides considerably lower prediction and estimation errors.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:24:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 26. Solving Kernel Ridge Regression with Gradient Descent for a Non-Constant Kernel Allerbo, Oskar PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_25_j_idt669",{id:"formSmash:items:resultList:25:j_idt669",widgetVar:"widget_formSmash_items_resultList_25_j_idt669",onLabel:"Allerbo, Oskar ",offLabel:"Allerbo, Oskar ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:25:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:25:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Solving Kernel Ridge Regression with Gradient Descent for a Non-Constant Kernel2023Manuskript (preprint) (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_25_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:25:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_25_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Kernel ridge regression, KRR, is a generalization of linear ridge regression that is non-linear in the data, but linear in the parameters. The solution can be obtained either as a closed-form solution, which includes a matrix inversion, or iteratively through gradient descent. Using the iterative approach opens up for changing the kernel during training, something that is investigated in this paper. We theoretically address the effects this has on model complexity and generalization. Based on our findings, we propose an update scheme for the bandwidth of translational-invariant kernels, where we let the bandwidth decrease to zero during training, thus circumventing the need for hyper-parameter selection. We demonstrate on real and synthetic data how decreasing the bandwidth during training outperforms using a constant bandwidth, selected by cross-validation and marginal likelihood maximization. We also show theoretically and empirically that using a decreasing bandwidth, we are able to achieve both zero training error in combination with good generalization, and a double descent behavior, phenomena that do not occur for KRR with constant bandwidth but are known to appear for neural networks.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:25:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 27. Solving Kernel Ridge Regression with Gradient-Based Optimization Methods Allerbo, Oskar PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_26_j_idt669",{id:"formSmash:items:resultList:26:j_idt669",widgetVar:"widget_formSmash_items_resultList_26_j_idt669",onLabel:"Allerbo, Oskar ",offLabel:"Allerbo, Oskar ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:26:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:26:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Solving Kernel Ridge Regression with Gradient-Based Optimization MethodsManuskript (preprint) (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_26_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:26:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_26_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Kernel ridge regression, KRR, is a generalization of linear ridge regression that is non-linear in the data, but linear in the parameters. Here, we introduce an equivalent formulation of the objective function of KRR, opening up both for using penalties other than the ridge penalty and for studying kernel ridge regression from the perspective of gradient descent. Using a continuous-time perspective, we derive a closed-form solution for solving kernel regression with gradient descent, something we refer to as kernel gradient flow, KGF, and theoretically bound the differences between KRR and KGF, where, for the latter, regularization is obtained through early stopping. We also generalize KRR by replacing the ridge penalty with the ℓ1 and ℓ∞ penalties, respectively, and use the fact that analogous to the similarities between KGF and KRR, ℓ1 regularization and forward stagewise regression (also known as coordinate descent), and ℓ∞ regularization and sign gradient descent, follow similar solution paths. We can thus alleviate the need for computationally heavy algorithms based on proximal gradient descent. We show theoretically and empirically how the ℓ1 and ℓ∞ penalties, and the corresponding gradient-based optimization algorithms, produce sparse and robust kernel regression solutions, respectively.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:26:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 28. Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control Allerbo, Oskar PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_27_j_idt669",{id:"formSmash:items:resultList:27:j_idt669",widgetVar:"widget_formSmash_items_resultList_27_j_idt669",onLabel:"Allerbo, Oskar ",offLabel:"Allerbo, Oskar ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_27_j_idt672",{id:"formSmash:items:resultList:27:j_idt672",widgetVar:"widget_formSmash_items_resultList_27_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:27:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Jörnsten, RebeckaKTH.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:27:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian ControlManuskript (preprint) (Annet vitenskapelig)29. Flexible, non-parametric modeling using regularized neural networks Allerbo, Oskar PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_28_j_idt669",{id:"formSmash:items:resultList:28:j_idt669",widgetVar:"widget_formSmash_items_resultList_28_j_idt669",onLabel:"Allerbo, Oskar ",offLabel:"Allerbo, Oskar ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_28_j_idt672",{id:"formSmash:items:resultList:28:j_idt672",widgetVar:"widget_formSmash_items_resultList_28_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); University of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:28:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Jörnsten, RebeckaUniversity of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:28:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Flexible, non-parametric modeling using regularized neural networks2022Inngår i: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 37, nr 4, s. 2029-2047Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_28_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:28:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_28_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Non-parametric, additive models are able to capture complex data dependencies in a flexible, yet interpretable way. However, choosing the format of the additive components often requires non-trivial data exploration. Here, as an alternative, we propose PrAda-net, a one-hidden-layer neural network, trained with proximal gradient descent and adaptive lasso. PrAda-net automatically adjusts the size and architecture of the neural network to reflect the complexity and structure of the data. The compact network obtained by PrAda-net can be translated to additive model components, making it suitable for non-parametric statistical modelling with automatic model selection. We demonstrate PrAda-net on simulated data, where we compare the test error performance, variable importance and variable subset identification properties of PrAda-net to other lasso-based regularization approaches for neural networks. We also apply PrAda-net to the massive U.K. black smoke data set, to demonstrate how PrAda-net can be used to model complex and heterogeneous data with spatial and temporal components. In contrast to classical, statistical non-parametric approaches, PrAda-net requires no preliminary modeling to select the functional forms of the additive components, yet still results in an interpretable model representation.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:28:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 30. Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders Allerbo, Oskar PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_29_j_idt669",{id:"formSmash:items:resultList:29:j_idt669",widgetVar:"widget_formSmash_items_resultList_29_j_idt669",onLabel:"Allerbo, Oskar ",offLabel:"Allerbo, Oskar ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_29_j_idt672",{id:"formSmash:items:resultList:29:j_idt672",widgetVar:"widget_formSmash_items_resultList_29_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Chalmers university.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:29:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Jörnsten, RebeckaChalmers university.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:29:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders2021Inngår i: Journal of machine learning research, ISSN 1532-4435, E-ISSN 1533-7928, Vol. 22, s. 1-28Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_29_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:29:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_29_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); High-dimensional data sets are often analyzed and explored via the construction of a latent low-dimensional space which enables convenient visualization and efficient predictive modeling or clustering. For complex data structures, linear dimensionality reduction techniques like PCA may not be sufficiently flexible to enable low-dimensional representation. Non-linear dimension reduction techniques, like kernel PCA and autoencoders, suffer from loss of interpretability since each latent variable is dependent of all input dimensions. To address this limitation, we here present path lasso penalized autoencoders. This structured regularization enhances interpretability by penalizing each path through the encoder from an input to a latent variable, thus restricting how many input variables are represented in each latent dimension. Our algorithm uses a group lasso penalty and non-negative matrix factorization to construct a sparse, non-linear latent representation. We compare the path lasso regularized autoencoder to PCA, sparse PCA, autoencoders and sparse autoencoders on real and simulated data sets. We show that the algorithm exhibits much lower reconstruction errors than sparse PCA and parameter-wise lasso regularized autoencoders for low-dimensional representations. Moreover, path lasso representations provide a more accurate reconstruction match, i.e. preserved relative distance between objects in the original and reconstructed spaces.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:29:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 31. First critical probability for a problem on random orientations in G(n,p) Alm, Sven Ericket al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_30_j_idt672",{id:"formSmash:items:resultList:30:j_idt672",widgetVar:"widget_formSmash_items_resultList_30_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:30:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Janson, SvanteLinusson, SvanteKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:30:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); First critical probability for a problem on random orientations in G(n,p)2014Inngår i: Electronic Journal of Probability, E-ISSN 1083-6489, Vol. 19, s. 69-Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_30_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:30:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_30_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We study the random graph G (n,p) with a random orientation. For three fixed vertices s, a, b in G(n,p) we study the correlation of the events {a -> s} (there exists a directed path from a to s) and {s -> b}. We prove that asymptotically the correlation is negative for small p, p < C-1/n, where C-1 approximate to 0.3617, positive for C-1/n < p < 2/n and up to p = p(2)(n). Computer aided computations suggest that p(2)(n) = C-2/n, with C-2 approximate to 7.5. We conjecture that the correlation then stays negative for p up to the previously known zero at 1/2; for larger p it is positive.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:30:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 32. Using Multiple Linear Regression to Estimate Customer Profitability in Consumer Credits Almgren, Andreas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_31_j_idt669",{id:"formSmash:items:resultList:31:j_idt669",widgetVar:"widget_formSmash_items_resultList_31_j_idt669",onLabel:"Almgren, Andreas ",offLabel:"Almgren, Andreas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:31:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:31:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Using Multiple Linear Regression to Estimate Customer Profitability in Consumer Credits2021Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_31_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:31:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_31_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In cooperation with a consumer credit company based in Stockholm, this bachelor thesis investigates if the customer profitability in the consumer credit market can be predicted with multiple linear regression. Data collected before the initial credit was accepted and data connected to the account activity of the customers' first nine months are analyzed. Further, it is examined if the findings could be useful in a profitability analysis and as a reduction of adverse selection.

The findings show that a number of covariates express promising correlations with the costumer profitability. However, the prediction error is high and not efficient in individual cases. Further, some reduction in adverse selection, due to a decrease in asymmetric information between the customers and the company, can be identified, but further research is encouraged. Finally, potential improvements are discussed, especially concerning the choice of regression algorithm.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:31:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_31_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:31:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_31_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:31:j_idt946:0:fullText"});}); 33. Evaluation of HYDRA - A risk model for hydropower plants Almgren, Lars PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_32_j_idt669",{id:"formSmash:items:resultList:32:j_idt669",widgetVar:"widget_formSmash_items_resultList_32_j_idt669",onLabel:"Almgren, Lars ",offLabel:"Almgren, Lars ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:32:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:32:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Evaluation of HYDRA - A risk model for hydropower plants2016Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_32_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:32:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_32_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Vattenfall Hydro AB has more than 50 large scale power plants. In these power plants there are over 130 power generating units. The planning of renewals of these units is important to minimize the risk of having big breakdowns which inflict long downtime. Because all power plants are different Vattenfall Hydro AB started using a self developed risk model in 2003 to improve the comparisons between power plants. Since then the model has been used without larger improvements or validation.

The purpose of this study is to evaluate and analyse how well the risk model has performed and is performing. This thesis is divided into five subsections where analyses are made on the input to the model, adverse events used in the model, the probabilities used in the model, risk forecasts from the model and finally trends for the periods the model has been used. In each subsection different statistical methods are used for the analyses.

From the analyses it is clear that the low number of adverse events in power plants makes the usage of statistical methods for evaluating performance of Vattenfall Hydro AB’s risk model imprecise. Based on the results of this thesis the conclusion is made that if the risk model is to be used in the future it needs further improvements to generate more accurate results.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:32:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_32_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:32:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_32_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:32:j_idt946:0:fullText"});}); 34. Specious randomness data sequences for various number systems Almlöf, JonasPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:33:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Björk, GunnarKTH, Skolan för teknikvetenskap (SCI), Tillämpad fysik.Vall-Llosera, GemmaEricsson.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:33:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Specious randomness data sequences for various number systems2021DatasetAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_33_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:33:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_33_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The files contain randomly-ordered N-number system elements where N=13,16,17, 24 and 25. For N=24, two such sequences were concatenated (each with a different random order).

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:33:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (zip)data set$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_33_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:33:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_33_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:33:j_idt946:0:fullText"});}); 35. Modeling Patterns of Transactions after Companies Implementation of Getswish AB’s Payment Service Amaya Scott, Jakob PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_34_j_idt669",{id:"formSmash:items:resultList:34:j_idt669",widgetVar:"widget_formSmash_items_resultList_34_j_idt669",onLabel:"Amaya Scott, Jakob ",offLabel:"Amaya Scott, Jakob ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_34_j_idt672",{id:"formSmash:items:resultList:34:j_idt672",widgetVar:"widget_formSmash_items_resultList_34_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:34:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Skålberg, AmandaKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:34:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Modeling Patterns of Transactions after Companies Implementation of Getswish AB’s Payment Service2022Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_34_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:34:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_34_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This thesis is a case study in collaboration with the company Getswish AB. GetswishAB provides the mobile application and payment service Swish with the purpose ofdelivering smooth money transfers for individuals and companies in Sweden. About80 percent of the Swedish population are connected to Swish, and the majority seethe service as an apparent part of everyday life. This work studies a small part of alltransactions that take place daily between individuals and companies. Specifically, thispaper examines which factors affect the Swish transaction amount (TA) to companieswithin five different industries. The five industries studied are: Sports, leisure,and entertainment activities; Restaurant, catering, and bar activities; Retail trade,except for motor vehicles and motorcycles; Trade and repair of motor vehicles andmotorcycles; and Telecommunications. In combination with descriptive analysis andseasonality studies, a multiple linear regression model is used to evaluate patternsin the amount transferred to companies within the various industries. The responsevariable is the daily aggregated TA and the seven responding regressors examined are:i) The number of employees of the company, ii) The revenue of the company, iii) Thedate for registration to Swish service for companies, iv) The age of the customers, v) Thegender of the customers, vi) The number of transactions, and vii) The transaction date.The estimated parameters for each regressor are studied to evaluate correlations withthe TA. This thesis states that it is possible to construct a model from the regressorsanalyzed, which can predict the amount with an explanation degree of above 85% forfour of the five industries. The model constructed for the motor vehicle industry nevergives satisfactory results and must be further investigated to conclude.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:34:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_34_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:34:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_34_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:34:j_idt946:0:fullText"});}); 36. The Maximum Likelihood Degree Of Linear Spaces Of Symmetric Matrices Amendola, Carlos PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_35_j_idt669",{id:"formSmash:items:resultList:35:j_idt669",widgetVar:"widget_formSmash_items_resultList_35_j_idt669",onLabel:"Amendola, Carlos ",offLabel:"Amendola, Carlos ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_35_j_idt672",{id:"formSmash:items:resultList:35:j_idt672",widgetVar:"widget_formSmash_items_resultList_35_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Tech Univ Munich, Munich, Germany..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:35:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Gustafsson, LukasKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).Kohn, KathlénKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).Marigliano, OrlandoKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).Seigal, AnnaHarvard Univ, Cambridge, MA 02138 USA..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:35:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); The Maximum Likelihood Degree Of Linear Spaces Of Symmetric Matrices2021Inngår i: Le Matematiche, ISSN 2037-5298, E-ISSN 0373-3505, Vol. 76, nr 2, s. 535-557Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_35_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:35:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_35_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We study multivariate Gaussian models that are described by linear conditions on the concentration matrix. We compute the maximum likelihood (ML) degrees of these models. That is, we count the critical points of the likelihood function over a linear space of symmetric matrices. We obtain new formulae for the ML degree, one via line geometry, and another using Segre classes from intersection theory. We settle the case of codimension one models, and characterize the degenerate case when the ML degree is zero.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:35:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 37. Sales Volume Forecasting of Ericsson Radio Units - A Statistical Learning Approach Amethier, Patrik PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_36_j_idt669",{id:"formSmash:items:resultList:36:j_idt669",widgetVar:"widget_formSmash_items_resultList_36_j_idt669",onLabel:"Amethier, Patrik ",offLabel:"Amethier, Patrik ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_36_j_idt672",{id:"formSmash:items:resultList:36:j_idt672",widgetVar:"widget_formSmash_items_resultList_36_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:36:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Gerbaulet, AndréKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:36:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Sales Volume Forecasting of Ericsson Radio Units - A Statistical Learning Approach2020Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_36_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:36:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_36_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Demand forecasting is a well-established internal process at Ericsson, where employees from various departments within the company collaborate in order to predict future sales volumes of specific products over horizons ranging from months to a few years. This study aims to evaluate current predictions regarding radio unit products of Ericsson, draw insights from historical volume data, and finally develop a novel, statistical prediction approach. Specifically, a two-part statistical model with a decision tree followed by a neural network is trained on previous sales data of radio units, and then evaluated (also on historical data) regarding predictive accuracy. To test the hypothesis that mid-range volume predictions of a 1-3 year horizon made by data-driven statistical models can be more accurate, the two-part model makes predictions per individual radio unit product based on several predictive attributes, mainly historical volume data and information relating to geography, country and customer trends.

The majority of wMAPEs per product from the predictive model were shown to be less than 5% for the three different prediction horizons, which can be compared to global wMAPEs from Ericsson's existing long range forecast process of 9% for 1 year, 13% for 2 years and 22% for 3 years. These results suggest the strength of the data-driven predictive model. However, care must be taken when comparing the two error measures and one must take into account the large variances of wMAPEs from the predictive model.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:36:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_36_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:36:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_36_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:36:j_idt946:0:fullText"});}); 38. Disk counting statistics near hard edges of random normal matrices: The multi-component regime Ameur, Yacin PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_37_j_idt669",{id:"formSmash:items:resultList:37:j_idt669",widgetVar:"widget_formSmash_items_resultList_37_j_idt669",onLabel:"Ameur, Yacin ",offLabel:"Ameur, Yacin ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_37_j_idt672",{id:"formSmash:items:resultList:37:j_idt672",widgetVar:"widget_formSmash_items_resultList_37_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Centre for Mathematical Sciences, Lund University, 22100 Lund, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:37:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Charlier, ChristopheCentre for Mathematical Sciences, Lund University, 22100 Lund, Sweden.Cronvall, JoakimCentre for Mathematical Sciences, Lund University, 22100 Lund, Sweden.Lenells, JonatanKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:37:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Disk counting statistics near hard edges of random normal matrices: The multi-component regime2024Inngår i: Advances in Mathematics, ISSN 0001-8708, E-ISSN 1090-2082, Vol. 441, artikkel-id 109549Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_37_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:37:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_37_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We consider a two-dimensional point process whose points are separated into two disjoint components by a hard wall, and study the multivariate moment generating function of the corresponding disk counting statistics. We investigate the “hard edge regime” where all disk boundaries are a distance of order [Formula presented] away from the hard wall, where n is the number of points. We prove that as n→+∞, the asymptotics of the moment generating function are of the form [Formula presented] and we determine the constants C1,…,C4 explicitly. The oscillatory term Fn is of order 1 and is given in terms of the Jacobi theta function. Our theorem allows us to derive various precise results on the disk counting function. For example, we prove that the asymptotic fluctuations of the number of points in one component are of order 1 and are given by an oscillatory discrete Gaussian. Furthermore, the variance of this random variable enjoys asymptotics described by the Weierstrass ℘-function.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:37:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 39. Exponential moments for disk counting statistics at the hard edge of random normal matrices Ameur, Yacin PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt669",{id:"formSmash:items:resultList:38:j_idt669",widgetVar:"widget_formSmash_items_resultList_38_j_idt669",onLabel:"Ameur, Yacin ",offLabel:"Ameur, Yacin ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt672",{id:"formSmash:items:resultList:38:j_idt672",widgetVar:"widget_formSmash_items_resultList_38_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Centre for Mathematical Sciences, Lund University, Box 118, 22100 Lund, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:38:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Charlier, ChristopheCentre for Mathematical Sciences, Lund University, Box 118, 22100 Lund, Sweden.Cronvall, JoakimCentre for Mathematical Sciences, Lund University, Box 118, 22100 Lund, Sweden.Lenells, JonatanKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:38:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Exponential moments for disk counting statistics at the hard edge of random normal matrices2023Inngår i: Journal of Spectral Theory, ISSN 1664-039X, E-ISSN 1664-0403, Vol. 13, nr 3, s. 841-902Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:38:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_38_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We consider the multivariate moment generating function of the disk counting statistics of a model Mittag-Leffler ensemble in the presence of a hard wall. Let n be the number of points. We focus on two regimes: (a) the “hard edge regime” where all disk boundaries are at a distance of order n1 from the hard wall, and (b) the “semi-hard edge regime” where all disk boundaries are at a distance of order √1n from the hard wall. As n → + ∞, we prove that the moment generating function enjoys asymptotics of the form (Equation presented) In both cases, we determine the constants C1;:::; C4 explicitly. We also derive precise asymptotic formulas for all joint cumulants of the disk counting function, and establish several central limit theorems. Surprisingly, and in contrast to the “bulk”, “soft edge”, and “semi-hard edge” regimes, the second and higher order cumulants of the disk counting function in the “hard edge” regime are proportional to n and not to √n.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:38:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 40. Random normal matrices and ward identities Ameur, Yacinet al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_39_j_idt672",{id:"formSmash:items:resultList:39:j_idt672",widgetVar:"widget_formSmash_items_resultList_39_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:39:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hedenmalm, HåkanKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).Makarov, NikolaiPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:39:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Random normal matrices and ward identities2015Inngår i: Annals of Probability, ISSN 0091-1798, E-ISSN 2168-894X, Vol. 43, nr 3, s. 1157-1201Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_39_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:39:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_39_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We consider the random normal matrix ensemble associated with a potential in the plane of sufficient growth near infinity. It is known that asymptotically as the order of the random matrix increases indefinitely, the eigenvalues approach a certain equilibrium density, given in terms of Frostman's solution to the minimum energy problem of weighted logarithmic potential theory. At a finer scale, we may consider fluctuations of eigenvalues about the equilibrium. In the present paper, we give the correction to the expectation of the fluctuations, and we show that the potential field of the corrected fluctuations converge on smooth test functions to a Gaussian free field with free boundary conditions on the droplet associated with the potential.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:39:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 41. A comparison between different volatility models Amsköld, Daniel PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_40_j_idt669",{id:"formSmash:items:resultList:40:j_idt669",widgetVar:"widget_formSmash_items_resultList_40_j_idt669",onLabel:"Amsköld, Daniel ",offLabel:"Amsköld, Daniel ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:40:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:40:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A comparison between different volatility models2011Independent thesis Advanced level (degree of Master (One Year)), 20 poäng / 30 hpOppgaveFulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_40_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:40:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_40_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:40:j_idt946:0:fullText"});}); 42. Differential Equations for Gaussian Statistical Models with Rational Maximum Likelihood Estimator Améndola, Carloset al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_41_j_idt672",{id:"formSmash:items:resultList:41:j_idt672",widgetVar:"widget_formSmash_items_resultList_41_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:41:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Gustafsson, LukasKohn, KathlénKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).Marigliano, OrlandoSeigal, AnnaPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:41:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Differential Equations for Gaussian Statistical Models with Rational Maximum Likelihood Estimator: arXiv: 2304.12054Manuskript (preprint) (Annet vitenskapelig)43. Invariant theory and scaling algorithms for maximum likelihood estimation Améndola, Carlos PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_42_j_idt669",{id:"formSmash:items:resultList:42:j_idt669",widgetVar:"widget_formSmash_items_resultList_42_j_idt669",onLabel:"Améndola, Carlos ",offLabel:"Améndola, Carlos ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_42_j_idt672",{id:"formSmash:items:resultList:42:j_idt672",widgetVar:"widget_formSmash_items_resultList_42_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Technische Universität Berlin, Berlin, 10623, Germany.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:42:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Kohn, KathlénKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Algebra, kombinatorik och topologi.Reichenbach, PhilippTechnische Universität Berlin, Berlin, 10623, Germany.Seigal, AnnaTechnische Universität Berlin, Berlin, 10623, Germany.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:42:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Invariant theory and scaling algorithms for maximum likelihood estimation2021Inngår i: SIAM Journal on Applied Algebra and Geometry, ISSN 2470-6566, Vol. 5, nr 2, s. 304-337Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_42_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:42:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_42_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We uncover connections between maximum likelihood estimation in statistics and norm minimization over a group orbit in invariant theory. We focus on Gaussian transformation families, which include matrix normal models and Gaussian graphical models given by transitive directed acyclic graphs. We use stability under group actions to characterize boundedness of the likelihood, and existence and uniqueness of the maximum likelihood estimate. Our approach reveals promising consequences of the interplay between invariant theory and statistics. In particular, existing scaling algorithms from statistics can be used in invariant theory, and vice versa.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:42:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 44. Smart Beta Investering Baserad på Makroekonomiska Indikatorer Andersson, Alexandra PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_43_j_idt669",{id:"formSmash:items:resultList:43:j_idt669",widgetVar:"widget_formSmash_items_resultList_43_j_idt669",onLabel:"Andersson, Alexandra ",offLabel:"Andersson, Alexandra ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:43:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:43:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Smart Beta Investering Baserad på Makroekonomiska Indikatorer2015Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_43_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:43:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_43_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This thesis examines the possibility to find a relationship between the Nasdaq Nordea Smart Beta Indices and a series of macroeconomic indicators. This relationship will be used as a signal-value and implemented in a portfolio consisting of all six smart beta indices. To investigate the impact of the signal-value on the portfolio performance, three portfolio strategies are examined with the equally weighted portfolio as a benchmark. The portfolio weights will be re-evaluated monthly and the portfolios examined are the mean-variance portfolio, the mean-variance portfolio based on the signal-value and the equally weighted portfolio based on the signal-value.

In order to forecast the performance of the portfolio, a multivariate GARCH model with time-varying correlations is fitted to the data and three different error-distributions are considered. The performances of the portfolios are studied both in- and out-of-sample and the analysis is based on the Sharpe ratio.

The results indicate that a mean-variance portfolio based on the relationship with the macroeconomic indicators outperforms the other portfolios for the in-sample period, with respect to the Sharpe ratio. In the out-of-sample period however, none of the portfolio strategies has Sharpe ratios that are statistically different from that of an equally weighted portfolio.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:43:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_43_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:43:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_43_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:43:j_idt946:0:fullText"});}); 45. Portfolio Performance Optimization Using Multivariate Time Series Volatilities Processed With Deep Layering LSTM Neurons and Markowitz Andersson, Aron PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_44_j_idt669",{id:"formSmash:items:resultList:44:j_idt669",widgetVar:"widget_formSmash_items_resultList_44_j_idt669",onLabel:"Andersson, Aron ",offLabel:"Andersson, Aron ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_44_j_idt672",{id:"formSmash:items:resultList:44:j_idt672",widgetVar:"widget_formSmash_items_resultList_44_j_idt672",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:44:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Mirkhani, ShabnamKTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:44:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Portfolio Performance Optimization Using Multivariate Time Series Volatilities Processed With Deep Layering LSTM Neurons and Markowitz2020Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_44_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:44:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_44_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The stock market is a non-linear field, but many of the best-known portfolio optimization algorithms are based on linear models. In recent years, the rapid development of machine learning has produced flexible models capable of complex pattern recognition. In this paper, we propose two different methods of portfolio optimization; one based on the development of a multivariate time-dependent neural network,thelongshort-termmemory(LSTM),capable of finding lon gshort-term price trends. The other is the linear Markowitz model, where we add an exponential moving average to the input price data to capture underlying trends. The input data to our neural network are daily prices, volumes and market indicators such as the volatility index (VIX).The output variables are the prices predicted for each asset the following day, which are then further processed to produce metrics such as expected returns, volatilities and prediction error to design a portfolio allocation that optimizes a custom utility function like the Sharpe Ratio. The LSTM model produced a portfolio with a return and risk that was close to the actual market conditions for the date in question, but with a high error value, indicating that our LSTM model is insufficient as a sole forecasting tool. However,the ability to predict upward and downward trends was somewhat better than expected and therefore we conclude that multiple neural network can be used as indicators, each responsible for some specific aspect of what is to be analysed, to draw a conclusion from the result. The findings also suggest that the input data should be more thoroughly considered, as the prediction accuracy is enhanced by the choice of variables and the external information used for training.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:44:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)fulltext$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_44_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:44:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_44_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:44:j_idt946:0:fullText"});}); 46. A mixed relaxed singular maximum principle for linear SDEs with random coefficients Andersson, Daniel PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_45_j_idt669",{id:"formSmash:items:resultList:45:j_idt669",widgetVar:"widget_formSmash_items_resultList_45_j_idt669",onLabel:"Andersson, Daniel ",offLabel:"Andersson, Daniel ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:45:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:45:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A mixed relaxed singular maximum principle for linear SDEs with random coefficientsArtikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_45_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:45:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_45_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We study singular stochastic control of a two dimensional stochastic differential equation, where the first component is linear with random and unbounded coefficients. We derive existence of an optimal relaxed control and necessary conditions for optimality in the form of a mixed relaxed-singular maximum principle in a global form. A motivating example is given in the form of an optimal investment and consumption problem with transaction costs, where we consider a portfolio with a continuum of bonds and where the portfolio weights are modeled as measure-valued processes on the set of times to maturity.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:45:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 47. Contributions to the Stochastic Maximum Principle Andersson, Daniel PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_46_j_idt669",{id:"formSmash:items:resultList:46:j_idt669",widgetVar:"widget_formSmash_items_resultList_46_j_idt669",onLabel:"Andersson, Daniel ",offLabel:"Andersson, Daniel ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:46:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:46:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Contributions to the Stochastic Maximum Principle2009Doktoravhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_46_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:46:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_46_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This thesis consists of four papers treating the maximum principle for stochastic control problems.

In the first paper we study the optimal control of a class of stochastic differential equations (SDEs) of mean-field type, where the coefficients are allowed to depend on the law of the process. Moreover, the cost functional of the control problem may also depend on the law of the process. Necessary and sufficient conditions for optimality are derived in the form of a maximum principle, which is also applied to solve the mean-variance portfolio problem.

In the second paper, we study the problem of controlling a linear SDE where the coefficients are random and not necessarily bounded. We consider relaxed control processes, i.e. the control is defined as a process taking values in the space of probability measures on the control set. The main motivation is a bond portfolio optimization problem. The relaxed control processes are then interpreted as the portfolio weights corresponding to different maturity times of the bonds. We establish existence of an optimal control and necessary conditons for optimality in the form of a maximum principle, extended to include the family of relaxed controls.

The third paper generalizes the second one by adding a singular control process to the SDE. That is, the control is singular with respect to the Lebesgue measure and its influence on the state is thus not continuous in time. In terms of the portfolio problem, this allows us to consider two investment possibilities - bonds (with a continuum of maturities) and stocks - and incur transaction costs between the two accounts.

In the fourth paper we consider a general singular control problem. The absolutely continuous part of the control is relaxed in the classical way, i.e. the generator of the corresponding martingale problem is integrated with respect to a probability measure, guaranteeing the existence of an optimal control. This is shown to correspond to an SDE driven by a continuous orthogonal martingale measure. A maximum principle which describes necessary conditions for optimal relaxed singular control is derived.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:46:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)FULLTEXT01$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_46_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:46:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_46_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:46:j_idt946:0:fullText"});}); 48. Necessary Optimality Conditions for Two Stochastic Control Problems Andersson, Daniel PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_47_j_idt669",{id:"formSmash:items:resultList:47:j_idt669",widgetVar:"widget_formSmash_items_resultList_47_j_idt669",onLabel:"Andersson, Daniel ",offLabel:"Andersson, Daniel ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:47:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:47:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Necessary Optimality Conditions for Two Stochastic Control Problems2008Licentiatavhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_47_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:47:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_47_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This thesis consists of two papers concerning necessary conditions in stochastic control problems. In the first paper, we study the problem of controlling a linear stochastic differential equation (SDE) where the coefficients are random and not necessarily bounded. We consider relaxed control processes, i.e. the control is defined as a process taking values in the space of probability measures on the control set. The main motivation is a bond portfolio optimization problem. The relaxed control processes are then interpreted as the portfolio weights corresponding to different maturity times of the bonds. We establish existence of an optimal control and necessary conditions for optimality in the form of a maximum principle, extended to include the family of relaxed controls.

In the second paper we consider the so-called singular control problem where the control consists of two components, one absolutely continuous and one singular. The absolutely continuous part of the control is allowed to enter both the drift and diffusion coefficient. The absolutely continuous part is relaxed in the classical way, i.e. the generator of the corresponding martingale problem is integrated with respect to a probability measure, guaranteeing the existence of an optimal control. This is shown to correspond to an SDE driven by a continuous orthogonal martingale measure. A maximum principle which describes necessary conditions for optimal relaxed singular control is derived.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:47:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fulltekst (pdf)FULLTEXT01$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_47_j_idt946_0_j_idt949",{id:"formSmash:items:resultList:47:j_idt946:0:j_idt949",widgetVar:"widget_formSmash_items_resultList_47_j_idt946_0_j_idt949",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:47:j_idt946:0:fullText"});}); 49. The relaxed general maximum principle for singular optimal control of diffusions Andersson, Daniel PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_48_j_idt669",{id:"formSmash:items:resultList:48:j_idt669",widgetVar:"widget_formSmash_items_resultList_48_j_idt669",onLabel:"Andersson, Daniel ",offLabel:"Andersson, Daniel ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:48:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:48:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); The relaxed general maximum principle for singular optimal control of diffusions2009Inngår i: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, ISSN 01676911, Vol. 58, nr 1, s. 76-82Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_48_j_idt714_0_j_idt715",{id:"formSmash:items:resultList:48:j_idt714:0:j_idt715",widgetVar:"widget_formSmash_items_resultList_48_j_idt714_0_j_idt715",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In this paper we study optimality in stochastic control problems where the state process is a stochastic differential equation (SDE) and the control variable has two components, the first being absolutely continuous and the second singular. A control is defined as a solution to the corresponding martingale problem. To obtain existence of an optimal control Haussmann and Suo [U.G. Haussmann, W. Suo, Singular optimal stochastic controls I: Existence, SIAM J. Control Optim. 33 (3) (1995) 916-936] relaxed the martingale problem by extending the absolutely continuous control to the space of probability measures on the control set. Bahlali et al. [S. Bahlali, B. Djehiche, B. Mezerdi, The relaxed stochastic maximum principle in singular optimal control of diffusions, SIAM J. Control Optim. 46 (2) (2007) 427-444] established a maximum principle for relaxed singular control problems with uncontrolled diffusion coefficient. The main goal of this paper is to extend their results to the case where the control enters the diffusion coefficient. The proof is based on necessary conditions for near optimality of a sequence of ordinary controls which approximate the optimal relaxed control. The necessary conditions for near optimality are obtained by Ekeland's variational principle and the general maximum principle for (strict) singular control problems obtained in Bahlali and Mezerdi [S. Bahlali, B. Mezerdi, A general stochastic maximum principle for singular control problems, Electron J. Probab. 10 (2005) 988-1004. Paper no 30]. © 2008 Elsevier B.V. All rights reserved.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:48:j_idt714:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 50. The relaxed stochastic maximum principle in singular optimal control of diffusions with controlled diffusion coefficient Andersson, Daniel PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_49_j_idt669",{id:"formSmash:items:resultList:49:j_idt669",widgetVar:"widget_formSmash_items_resultList_49_j_idt669",onLabel:"Andersson, Daniel ",offLabel:"Andersson, Daniel ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.).PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:49:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:49:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); The relaxed stochastic maximum principle in singular optimal control of diffusions with controlled diffusion coefficientManuskript (Annet vitenskapelig)

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