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  • 1. Accoto, Nadia
    et al.
    Rydén, Tobias
    Lund University.
    Secchi, Paolo
    Bayesian Hidden Markov Models for Performance-Based Regulation of Continuity of Electricity Supply2010In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 25, no 3, p. 1236-1249Article in journal (Refereed)
    Abstract [en]

    A fundamental aspect in the regulation of the continuity of electricity supply is the identification of faults that could be caused by an exceptional event and, therefore, that are outside the utility control and responsibility. Different methods have been proposed during the years: the interpretation of the observed faults as a signal of an underlying system naturally leads to the analysis of the problem by means of a hidden Markov model. Thesemodels, in fact, are widely used for introducing dependence in data and/or for modeling observed phenomena depending on hidden processes. The application of this method shows that the model is able to identify exceptional events; moreover, the study of the estimated model parameters gives rise to reality-linked considerations.

  • 2.
    Andersson, Sofia
    et al.
    AstraZeneca R and D.
    Rydén, Tobias
    Lund University.
    Subspace estimation and prediction methods for hidden Markov models2009In: Annals of Statistics, ISSN 0090-5364, E-ISSN 2168-8966, Vol. 37, no 6B, p. 4131-4152Article in journal (Refereed)
    Abstract [en]

    Hidden Markov models (HMMs) are probabilistic functions of finite Markov chains, or, put in other words, state space models with finite state space. In this paper, we examine subspace estimation methods for HMMs whose output lies a finite set as well. In particular, we study the geometric structure arising from the nonminimality of the linear state space representation of HMMs, and consistency of a subspace algorithm arising from a certain factorization of the singular value decomposition of the estimated linear prediction matrix, For this algorithm, we show that the estimates of the transition and emission probability matrices are consistent up to a similarity transformation, and that the in-step linear predictor Computed from the estimated system matrices is consistent, i.e., converges to the true optimal linear m-step predictor.

  • 3. Asmussen, Sören
    et al.
    Rydén, Tobias
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    A Note on Skewness in Regenerative Simulation2011In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 40, no 1, p. 45-57Article in journal (Refereed)
    Abstract [en]

    The purpose of this article is to show, empirically and theoretically, that performance evaluation by means of regenerative simulation often involves random variables with distributions that are heavy tailed and heavily skewed. This, in turn, leads to the variance of estimators being poorly estimated, and confidence intervals having actual coverage quite different from (typically lower than) the nominal one. We illustrate these general ideas by estimating the mean occupancy and tail probabilities in M/G/1 queues, comparing confidence intervals computed from batch means to various intervals computed from regenerative cycles. In addition, we provide theoretical results on skewness to support the empirical findings.

  • 4. Bengtsson, Göran
    et al.
    Nilsson, Elna
    Rydén, Tobias
    Lund University.
    Wiktorsson, Magnus
    Irregular walks and loops combines in small-scale movement of a soil insect: implications for dispersal biology2004In: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 231, no 2, p. 299-306Article in journal (Refereed)
    Abstract [en]

    Analysis of small-scale movement patterns of animals we may help to understand and predict movement at a larger scale, such as dispersal, which is a key parameter in spatial population dynamics. We have chosen to study the movement of a soil-dwelling Collembola, Protaphorura armata, in an experimental system consisting of a clay surface with or without physical obstacles. A combination of video recordings, descriptive statistics, and walking simulations was used to evaluate the movement pattern. Individuals were found to link periods of irregular walk with those of looping in ahomogeneous environment as well as in one structured to heterogeneity by physical obstacles. The number of loops varied between 0 and 44 per hour from one individual to another and some individuals preferred to make loops by turning right and others by turning left. P. armata spent less time at the boundary of small obstacles compared to large, presumably because of a lower probability to track the steepness of the curvature as the individual walks along a highly curved surface. Food deprived P. armata had amore winding movement and made more circular loops than those that were well fed. The observed looping behaviour is interpreted in the context of systematic search strategies and compared with similar movement patterns found in other species.

  • 5. Bizjajeva, Svetlana
    et al.
    Rydén, Tobias
    Lund University.
    Edfors, Ove
    Mobile positioning in MIMO system using particle filtering2007In: 2007 IEEE 66TH VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-5, 2007, p. 792-798Conference paper (Refereed)
    Abstract [en]

    his paper represents the results of a simulation study on positioning of a mobile unit in MIMO settings. We used two different approaches for modeling the mobile movement, combined with a simple geometrical model for the MIMO channel. Three different particle filters were implemented for the position estimation. The results show that all three filters are able to achieve estimation accuracy required by Federal Communication Commission. The dimensionality of the particle filter state space is independent of the number of antenna elements, and it is possible to increase the number of antennas and use more sophisticated channel models without changing the filtering algorithms.

  • 6. Borg, S.
    et al.
    Gerdtham, U. G.
    Rydén, Tobias
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Munkholm, P.
    Odes, S.
    Langholz, E.
    Moum, B.
    Annese, V
    Bagnoli, S.
    Beltrami, M.
    Clofent, J.
    Friger, M.
    Milla, M.
    Mouzas, I
    O'Morain, C.
    Politi, P.
    Riis, L.
    Stockbrugger, R.
    Tsianos, E.
    Vardi, H.
    Lindgren, S.
    Estimation Of A Markov Chain For Crohn's Disease And Classification Of Patients Into Disease Phenotypes, In Eight Countries Using Individual Longitudinal Data Aggregated Over Time2012In: Value in Health, ISSN 1098-3015, E-ISSN 1524-4733, Vol. 15, no 7, p. A466-A467Article in journal (Other academic)
  • 7. Cappé, Olivier
    et al.
    Moulines, Eric
    Rydén, Tobias
    Lund University.
    Inference in Hidden Markov Models2005Book (Refereed)
  • 8. Douc, Randal
    et al.
    Moulines, Eric
    Rydén, Tobias
    Lund University.
    Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime2004In: Annals of Statistics, ISSN 0090-5364, E-ISSN 2168-8966, Vol. 32, no 5, p. 2254-2304Article in journal (Refereed)
    Abstract [en]

    An autoregressive process with Markov regime is an autoregressive process for which the regression function at each time point is given by a nonobservable Markov chain. In this paper we consider the asymptotic properties of the maximum likelihood estimator in a possibly nonstationary process of this kind for which the hidden state space is compact but not necessarily finite. Consistency and asymptotic normality are shown to follow from uniform exponential forgetting of the initial distribution for the hidden Markov chain conditional on the observations.

  • 9. Jensen, Christina T.
    et al.
    Böiers, Charlotta
    Kharazi, Shabnam
    Lübking, Anna
    Rydén, Tobias
    Lund University.
    Sigvardsson, Mikael
    Sitnicka, Ewa
    Jacobsen, Sten Eirik W.
    Permissive roles of hematopoietin and cytokine tyrosine kinase receptors in early T-cell development2008In: Blood, ISSN 0006-4971, E-ISSN 1528-0020, Vol. 111, p. 2083-2090Article in journal (Refereed)
    Abstract [en]

    Although several cytokineS have been demonstrated to be critical regulators of development of multiple blood cell lineages, it remains disputed to what degree they act through instructive or permissive mechanisms. Signaling through the FMS-like tyrosine kinase 3 (FLT3) receptor and the hematopoietin IL-7 receptor alpha (IL-7R alpha) has been demonstrated to be of critical importance for sustained thymopoiesis. Signaling triggered by IL-7 and thymic stromal lymphopoietin (TSLP) is dependent on IL-7R alpha, and both ligands adult mice doubly deficient in IL-7 and FLT3 ligand (FLT3L), TSLP does not play a key role in IL-7-independent or FLT3L-independent T lymphopoiesis. Furthermore, whereas previous studies implicated that the role of other cytokine tyrosine kinase receptors in T lymphopoiesis might not involve permissive actions, we demonstrate that ectopic expression of BCL2 is sufficient not only to partially correct the T-cell phenotype ofFIt3I(-/-) mice but also to rescue the virtually complete loss of all discernable stages of early T lymphopoiesis in FIt3I(-/-)II7r(-/-) mice. These findings implicate a permissive role of cytokine receptors of the hematopoietin and tyrosine kinase families in early T lymphopoiesis.

  • 10. Larsson, Sara
    et al.
    Rydén, Tobias
    Lund University.
    Holst, Ulla
    Oredsson, Stina
    Johansson, Maria
    Estimating the distribution of the G2 phase duration from flow cytometric histograms2008In: Mathematical Biosciences, ISSN 0025-5564, E-ISSN 1879-3134, Vol. 211, no 1, p. 1-17Article in journal (Refereed)
    Abstract [en]

    A mathematical model, based on branching processes, is proposed to interpret BrdUrd DNA FCM-derived data. Our main interest is in determining the distribution of the G(2) phase duration. Two different model classes involving different assumptions on the distribution of the G(2) phase duration are considered. Different assumptions of the G(2) phase duration result in very similar distributions of the S phase duration and the estimated means and standard deviations of the G(2) phase duration are all in the same range.

  • 11. Larsson, Sara
    et al.
    Rydén, Tobias
    Lund University.
    Holst, Ulla
    Oredsson, Stina
    Johansson, Maria
    Estimating the Total Rate of DNA Replication Using Branching Processes2008In: Bulletin of Mathematical Biology, ISSN 0092-8240, E-ISSN 1522-9602, Vol. 70, no 8, p. 2177-2194Article in journal (Refereed)
    Abstract [en]

    Increasing the knowledge of various cell cycle kinetic parameters, such as the length of the cell cycle and its different phases, is of considerable importance for several purposes including tumor diagnostics and treatment in clinical health care and a deepened understanding of tumor growth mechanisms. Of particular interest as a prognostic factor in different cancer forms is the S phase, during which DNA is replicated. In the present paper, we estimate the DNA replication rate and the S phase length from bromodeoxyuridine-DNA flow cytometry data. The mathematical analysis is based on a branching process model, paired with an assumed gamma distribution for the S phase duration, with which the DNA distribution of S phase cells can be expressed in terms of the DNA replication rate. Flow cytometry data typically contains rather large measurement variations, however, and we employ nonparametric deconvolution to estimate the underlying DNA distribution of S phase cells; an estimate of the DNA replication rate is then provided by this distribution and the mathematical model.

  • 12. Larsson, Sara
    et al.
    Rydén, Tobias
    Lund University.
    Holst, Ulla
    Oredsson, Stina
    Johansson, Maria
    Estimating the variation in S phase duration from flow cytometric histograms2008In: Mathematical Biosciences, ISSN 0025-5564, E-ISSN 1879-3134, Vol. 213, no 1, p. 40-49Article in journal (Refereed)
    Abstract [en]

    A stochastic model for interpreting BrdUrd DNA FCM-derived data is proposed. The model is based on branching processes and describes the progression of the DNA distribution of BrdUrd-labelled cells through the cell cycle. With the main focus on estimating the S phase duration and its variation, the DNA replication rate is modelled by a piecewise linear function, while assuming a gamma distribution for the S phase duration. Estimation of model parameters was carried out using maximum likelihood for data from two different cell lines. The results provided quite a good fit to the data, suggesting that stochastic models may be a valuable tool for analysing this kind of data.

  • 13. Lauss, Martin
    et al.
    Attila, Frigyesi
    Rydén, Tobias
    Lund University.
    Höglund, Mattias
    Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier2010In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 10, p. 532-Article in journal (Refereed)
    Abstract [en]

    Background: Genome wide gene expression data is a rich source for the identification of gene signatures suitable for clinical purposes and a number of statistical algorithms have been described for both identification and evaluation of such signatures. Some employed algorithms are fairly complex and hence sensitive to over-fitting whereas others are more simple and straight forward. Here we present a new type of simple algorithm based on ROC analysis and the use of metagenes that we believe will be a good complement to existing algorithms.Results: The basis for the proposed approach is the use of metagenes, instead of collections of individual genes, and a feature selection using AUC values obtained by ROC analysis. Each gene in a data set is assigned an AUC value relative to the tumor class under investigation and the genes are ranked according to these values. Metagenes are then formed by calculating the mean expression level for an increasing number of ranked genes, and the metagene expression value that optimally discriminates tumor classes in the training set is used for classification of new samples. The performance of the metagene is then evaluated using LOOCV and balanced accuracies.Conclusions: We show that the simple uni-variate gene expression average algorithm performs as well as several alternative algorithms such as discriminant analysis and the more complex approaches such as SVM and neural networks. The R package rocc is freely available at http://cran.r-project.org/web/packages/rocc/index.html.

  • 14. Lindgren, David
    et al.
    Frigyesi, Attila
    Gudjonsson, Sigurdur
    Sjödahl, Gottfrid
    Halldén, Christer
    Chebil, Gunilla
    Veerla, Srinivas
    Rydén, Tobias
    Lund University.
    Månsson, Wiking
    Liedberg, Fredrik
    Höglund, Mattias
    Combined Gene Expression and Genomic Profiling Define Two Intrinsic Molecular Subtypes of Urothelial Carcinoma and Gene Signatures for Molecular Grading and Outcome2010In: Cancer Research, ISSN 0008-5472, E-ISSN 1538-7445, Vol. 70, no 9, p. 3463-3472Article in journal (Refereed)
    Abstract [en]

    In the present investigation, we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes, we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1 in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated and defined by the same set of genes in three independent bladder cancer data sets. The two subtypes differed with respect to gene expression and mutation profiles, as well as with the level of genomic instability. The data show that genomic instability was the most distinguishing genomic feature of MS2 tumors, and that this trait was not dependent on TP53/MDM2 alterations. By combining molecular and pathologic data, it was possible to distinguish two molecular subtypes of T(a) and T(1) tumors, respectively. In addition, we define gene signatures validated in two independent data sets that classify urothelial carcinoma into low-grade (G(1)/G(2)) and high-grade (G(3)) tumors as well as non-muscle and muscle-invasive tumors with high precisions and sensitivities, suggesting molecular grading as a relevant complement to standard pathologic grading. We also present a gene expression signature with independent prognostic effect on metastasis and disease-specific survival. We conclude that the combination of molecular and histopathologic classification systems might provide a strong improvement for bladder cancer classification and produce new insights into the development of this tumor type.

  • 15. Lindoff, Bengt
    et al.
    Rydén, Tobias
    Lund University.
    Astely, David
    A robust cell search algorithm for 3GPP LTE2009In: 2009 European Wireless Conference, EW 2009, 2009, p. 303-307Conference paper (Refereed)
    Abstract [en]

    Mobility handling and performance is of major importance for the 3GPP Long Term Evolution (LTE) system. Therefore it is important for mobile devices to use reliable and robust algorithms for the cell search procedure, i.e. the procedure to detect neighbouring cells that can be used as potential handover candidates. We present a novel, low complexity, robust cell search scheme for LTE. The scheme uses a non-coherent approach and is therefore robust against Doppler spread and phase shifts due to sampling time instant mismatch, which makes it suitable for use in synchronized LTE TDD systems, as well as in high speed scenarios like high speed trains. This paper describes the theory behind the algorithm, and simulation results for LTE TDD showing its superior performance over coherent cell search algorithms are also presented.

  • 16. Maruotti, Antonello
    et al.
    Rydén, Tobias
    Lund University.
    A semiparametric approach to hidden Markov models under longitudinal observations2009In: Statistics and computing, ISSN 0960-3174, E-ISSN 1573-1375, Vol. 19, no 4, p. 381-393Article in journal (Refereed)
    Abstract [en]

    We propose a hidden Markov model for longitudinal count data where sources of unobserved heterogeneity arise, making data overdispersed. The observed process, conditionally on the hidden states, is assumed to follow an inhomogeneous Poisson kernel, where the unobserved heterogeneity is modeled in a generalized linear model (GLM) framework by adding individual-specific random effects in the link function. Due to the complexity of the likelihood within the GLM framework, model parameters may be estimated by numerical maximization of the log-likelihood function or by simulation methods; we propose a more flexible approach based on the Expectation Maximization (EM) algorithm. Parameter estimation is carried out using a non-parametric maximum likelihood (NPML) approach in a finite mixture context. Simulation results and two empirical examples are provided.

  • 17. Odille, Fabrice G. J.
    et al.
    Jonsson, Stefan
    Stjernqvist, Susann
    Rydén, Tobias
    Lund University.
    Wärnmark, Kenneth
    On the Characterization of Dynamic Supramolecular Systems: A General Mathematical Association Model for Linear Supramolecular Copolymers and Application on a Complex Two-Component Hydrogen-Bonding System2007In: Chemistry - A European Journal, ISSN 0947-6539, E-ISSN 1521-3765, Vol. 13, no 34, p. 9617-9636Article in journal (Refereed)
    Abstract [en]

    A general mathematical model for the characterization of the dynamic (kinetically labile) association of supramolecular assemblies in solution is presented. It is an extension of the equal K (EK) model by the stringent use of linear algebra to allow for the simultaneous presence of an unlimited number of different units in the resulting assemblies. It allows for the analysis of highly complex dynamic equilibrium systems in solution, including both supramolecular homo- and copolymers without the recourse to extensive approximations, in a field in which other analytical methods are difficult. The derived mathematical methodology makes it possible to analyze dynamic systems such as supramolecular copolymers regarding for instance the degree of polymerization, the distribution of a given monomer in different copolymers as well as its position in an aggregate. It is to date the only general means to characterize weak supramolecular systems. The model was fitted to NMR dilution titration data by using the program Matlab((R)), and a detailed algorithm for the optimization of the different parameters has been developed. The methodology is applied to a case study, a hydrogen-bonded supramolecular system, salen 4+porphyrin 5. The system is formally a two-component system but in reality a three-component system. This results in a complex dynamic system in which all monomers are associated to each other by hydrogen bonding with different association constants, resulting in homo- and copolymers 4,,5,, as well as cyclic structures 6 and 7, in addition to free 4 and 5. The system was analyzed by extensive NMR dilution titrations at variable temperatures. All chemical shifts observed at different temperatures were used in the fitting to obtain the Delta H degrees and Delta S degrees values producing the best global fit. From the derived general mathematical expressions, system 4+5 could be characterized with respect to above-mentioned parameters.

  • 18. Olsson, Jimmy
    et al.
    Rydén, Tobias
    Lund University.
    Asymptotic properties of particle filter-based maximum likelihood estimators for state space models2008In: Stochastic Processes and their Applications, ISSN 0304-4149, E-ISSN 1879-209X, Vol. 118, no 4, p. 649-680Article in journal (Refereed)
    Abstract [en]

    We study the asymptotic performance of approximate maximum likelihood estimators for state space models obtained via sequential Monte Carlo methods. The state space of the latent Markov chain and the parameter space are assumed to be compact. The approximate estimates are computed by, firstly, running possibly dependent particle filters on a fixed grid in the parameter space, yielding a pointwise approximation of the log-likelihood function. Secondly, extensions of this approximation to the whole parameter space are formed by means of piecewise constant functions or B-spline interpolation, and approximate maximum likelihood estimates are obtained through maximization of the resulting functions. In this setting we formulate criteria for how to increase the number of particles and the resolution of the grid in order to produce estimates that are consistent and asymptotically normal.

  • 19. Olsson, Jimmy
    et al.
    Rydén, Tobias
    Lund University.
    Particle filter-based approximate maximum likelihood inference asymptotics in state-space models2007In: ESAIM: Proc. Volume 19, 2007, Conference Oxford sur les méthodes de Monte Carlo séquentielles / [ed] Andrieu, C. and Crisan, D., 2007, p. 115-120Conference paper (Refereed)
    Abstract [en]

    To implement maximum likelihood estimation in state-space models, the log-likelihoodfunction must be approximated. We study such approximations based on particle filters, and in particularconditions for consistency of the corresponding approximate maximum likelihood estimator.Numerical results illustrate the theory.

  • 20. Olsson, Jimmy
    et al.
    Rydén, Tobias
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Rao-Blackwellization of Particle Markov Chain Monte Carlo Methods Using Forward Filtering Backward Sampling2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 10, p. 4606-4619Article in journal (Refereed)
    Abstract [en]

    Smoothing in state-space models amounts to computing the conditional distribution of the latent state trajectory, given observations, or expectations of functionals of the state trajectory with respect to this distribution. In recent years there has been an increased interest in Monte Carlo-based methods, often involving particle filters, for approximate smoothing in nonlinear and/or non-Gaussian state-space models. One such method is to approximate filter distributions using a particle filter and then to simulate, using backward kernels, a state trajectory backwards on the set of particles. We show that by simulating multiple realizations of the particle filter and adding a Metropolis-Hastings step, one obtains a Markov chain Monte Carlo scheme whose stationary distribution is the exact smoothing distribution. This procedure expands upon a similar one recently proposed by Andrieu, Doucet, Holenstein, and Whiteley. We also show that simulating multiple trajectories from each realization of the particle filter can be beneficial from a perspective of variance versus computation time, and illustrate this idea using two examples.

  • 21. Roueff, Francois
    et al.
    Rydén, Tobias
    Lund University.
    Non-parametric estimation of mixing densities for discrete distributions2005In: Annals of Statistics, ISSN 0090-5364, E-ISSN 2168-8966, Vol. 33, no 5, p. 2066-2108Article in journal (Refereed)
    Abstract [en]

    By a mixture density is meant a density of the form πμ(⋅)=∫πθ(⋅)×μ(dθ), where (πθ)θ∈Θ is a family of probability densities and μ is a probability measure on Θ. We consider the problem of identifying the unknown part of this model, the mixing distribution μ, from a finite sample of independent observations from πμ. Assuming that the mixing distribution has a density function, we wish to estimate this density within appropriate function classes. A general approach is proposed and its scope of application is investigated in the case of discrete distributions. Mixtures of power series distributions are more specifically studied. Standard methods for density estimation, such as kernel estimators, are available in this context, and it has been shown that these methods are rate optimal or almost rate optimal in balls of various smoothness spaces. For instance, these results apply to mixtures of the Poisson distribution parameterized by its mean. Estimators based on orthogonal polynomial sequences have also been proposed and shown to achieve similar rates. The general approach of this paper extends and simplifies such results. For instance, it allows us to prove asymptotic minimax efficiency over certain smoothness classes of the above-mentioned polynomial estimator in the Poisson case. We also study discrete location mixtures, or discrete deconvolution, and mixtures of discrete uniform distributions.

  • 22. Rubenthaler, Sylvain
    et al.
    Rydén, Tobias
    Lund University.
    Wiktorsson, Magnus
    Fast simulated annealing in Rd with an application to maximum likelihood estimation in state-space models2009In: Stochastic Processes and their Applications, ISSN 0304-4149, E-ISSN 1879-209X, Vol. 119, no 6, p. 1912-1931Article in journal (Refereed)
    Abstract [en]

    We study simulated annealing algorithms to maximise a function psi on a subset of R(d). In classical simulated annealing, given a current state theta(n) in stage n of the algorithm, the probability to accept a proposed state z at which psi is smaller, is exp(-beta(n+1)(psi(z) - psi (theta(n))) where (beta(n)) is the inverse temperature. With the standard logarithmic increase of (beta(n)) the probability P(psi(theta(n)) <= psi(max) - epsilon), with psi(max) the maximal value of psi, then tends to zero at a logarithmic rate as n increases. We examine variations of this scheme in which (beta(n)) is allowed to grow faster, but also consider other functions than the exponential for determining acceptance probabilities. The main result shows that faster rates of convergence can be obtained, both with the exponential and other acceptance functions. We also show how the algorithm may be applied to functions that cannot be computed exactly but only approximated, and give an example of maximising the log-likelihood function for a state-space model.

  • 23.
    Rydén, Tobias
    Lund University.
    EM versus Markov chain Monte Carlo for estimation of hidden Markov models: a computational perspective2008In: Bayesian Analysis, ISSN 1931-6690, Vol. 3, no 4, p. 659-688Article in journal (Refereed)
    Abstract [en]

    Hidden Markov models (HMMs) and related models have become standard in statistics during the last 15-20 years, with applications in diverse areas like speech and other statistical signal processing, hydrology, financial statistics and econometrics, bioinformatics etc. Inference in HMMs is traditionally often carried out using the EM algorithm, but examples of Bayesian estimation, in general implemented through Markov chain Monte Carlo (MCMC) sampling are also frequent in the HMM literature. The purpose of this paper is to compare the EM and MCMC approaches in three cases of different complexity; the examples include model order selection, continuous-time HMMs and variants of HMMs in which the observed data depends on many hidden variables in an overlapping fashion. All these examples in some way or another originate from real-data applications. Neither EM nor MCMC analysis of HMMs is a black-box methodology without need for user-interaction, and we will illustrate some of the problems, like poor mixing and long computation times, one may expect to encounter.

  • 24.
    Rydén, Tobias
    Lund University.
    Hidden Markov Models2004In: Encyclopedia of Actuarial Science: vol 2 / [ed] Teugels, J., and Sundt, B., Wiley-Blackwell, 2004, p. 821-827Chapter in book (Refereed)
  • 25. Sahlin, Ullrika
    et al.
    Rydén, Tobias
    Lund University.
    Nyberg, Cecilia D.
    Smith, Henrik G.
    A benefit analysis of screening for invasive species: Base-rate uncertainty and the value of information2011In: Methods in Ecology and Evolution, ISSN 2041-210X, E-ISSN 2041-210X, Vol. 2, no 5, p. 500-508Article in journal (Refereed)
    Abstract [en]

    1.. Implementation of the full spectra of screening tools to prevent the introduction of invasive species results in a need to evaluate the cost-efficiency of gathering the information needed to screen for these species. 2. We show how the Bayesian value of information approach can be used to derive the benefit of a screening model based on species traits, which in combination with the base rate of invasiveness, i.e. the proportion of invasive species among those introduced and established, predicts species-specific invasiveness. 3. A pre-posterior Bayesian analysis demonstrated that the benefit of the screening model of invasiveness depends on both the accuracy in predictions and the uncertainty in the base rate of invasiveness. However, even though increasing model accuracy always generates higher model benefit, acknowledging or neglecting the uncertainty in the base rate of invasiveness does not. This means that uncertainty in the base rate is important to consider in the cost-benefit analysis of the screening model. 4. As an example, we derived the benefit of basing decisions on a screening model trained for a data set on species traits of invasive and non-invasive marine macroalgae introduced into Europe. The benefit ranged from 0.6% to 19% of the loss of introducing an invasive species, where the actual value can be estimated if we know the monetary values of impacts from introducing invasive and not introducing non-invasive species. 5. Cost-benefit analyses of screening models for invasive species is one means to reach efficient management of the risks of non-indigenous species. Value of information is a useful tool for benefit analysis of predictive models with respect to decision-making, which goes beyond the investigations of model accuracy. Here, we use value of information analysis to evaluate which sources of uncertainty that is most worth while to reduce and how to set the cost of gathering further species-specific information which will improve the accuracy of a screening.

  • 26. Sköld, Martin
    et al.
    Rydén, Tobias
    Lund University.
    Samuelsson, Viktoria
    Bratt, Charlotte
    Ekblad, Lars
    Olsson, Håkan
    Baldetorp, Bo
    Regression analysis and modelling of data acquisition for SELDI-TOF mass spectrometry2007In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 23, no 11, p. 1401-1409Article in journal (Refereed)
    Abstract [en]

    Motivation: Pre-processing of SELDI-TOF mass spectrometry data is currently performed on a largel y ad hoc basis. This makes comparison of results from independent analyses troublesome and does not provide a framework for distinguishing different sources of variation in data. Results: In this article, we consider the task of pooling a large number of single-shot spectra, a task commonly performed automatically by the instrument software. By viewing the underlying statistical problem as one of heteroscedastic linear regression, we provide a framework for introducing robust methods and for dealing with missing data resulting from a limited span of recordable intensity values provided by the instrument. Our framework provides an interpretation of currently used methods as a maximum-likelihood estimator and allows theoretical derivation of its variance. We observe that this variance depends crucially on the total number of ionic species, which can vary considerably between different pooled spectra. This variation in variance can potentially invalidate the results from naive methods of discrimination/classification and we outline appropriate data transformations. Introducing methods from robust statistics did not improve the standard errors of the pooled samples. Imputing missing values however-using the EM algorithm-had a notable effect on the result; for our data, the pooled height of peaks which were frequently truncated increased by up to 30%.

  • 27. Stjernqvist, Susann
    et al.
    Rydén, Tobias
    Lund University.
    A continuous-index hidden Markov jump process for modeling DNA copy number data2009In: Biostatistics, ISSN 1465-4644, E-ISSN 1468-4357, Vol. 10, no 4, p. 773-778Article in journal (Refereed)
    Abstract [en]

    The number of copies of DNA in human cells can be measured using array comparative genomic hybridization (aCGH), which provides intensity ratios of sample to reference DNA at genomic locations corresponding to probes on a microarray. In the present paper, we devise a statistical model, based on a latent continuous-index Markov jump process, that is aimed to capture certain features of aCGH data, including probes that are unevenly long, unevenly spaced, and overlapping. The model has a continuous state space, with 1 state representing a normal copy number of 2, and the rest of the states being either amplifications or deletions. We adopt a Bayesian approach and apply Markov chain Monte Carlo (MCMC) methods for estimating the parameters and the Markov process. The model can be applied to data from both tiling bacterial artificial chromosome arrays and oligonucleotide arrays. We also compare a model with normal distributed noise to a model with t-distributed noise, showing that the latter is more robust to outliers.

  • 28. Stjernqvist, Susann
    et al.
    Rydén, Tobias
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Greenman, Chris D.
    Model-integrated estimation of normal tissue contamination for cancer SNP copy number data2011In: Cancer Informatics, ISSN 1176-9351, E-ISSN 1176-9351, Vol. 10, p. 159-173Article in journal (Refereed)
    Abstract [en]

    SNP allelic copy number data provides intensity measurements for the two different alleles separately. We present a method that estimates the number of copies of each allele at each SNP position, using a continuous-index hidden Markov model. The method is especially suited for cancer data, since it includes the fraction of normal tissue contamination, often present when studying data from cancer tumors, into the model. The continuous-index structure takes into account the distances between the SNPs, and is thereby appropriate also when SNPs are unequally spaced. In a simulation study we show that the method performs favorably compared to previous methods even with as much as 70% normal contamination. We also provide results from applications to clinical data produced using the Affymetrix genome-wide SNP 6.0 platform.

  • 29. Stjernqvist, Susann
    et al.
    Rydén, Tobias
    Lund University.
    Sköld, Martin
    Staaf, Johan
    Continuous-index hidden Markov modelling of array CGH copy number data2007In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 23, no 8, p. 1006-1014Article in journal (Refereed)
  • 30. Teranchi, Ramin
    et al.
    Woll, Petter S.
    Anderson, Kristina
    Buza-Vidas, Natalija
    Mizukami, Takuo
    Mead, Adam J.
    Åstrand-Grundström, Ingbritt
    Strömbeck, Bodil
    Horvat, Andrea
    Ferry, Helen
    Singh Dhanda, Rakesh
    Hast, Robert
    Rydén, Tobias
    Lund University.
    Vyas, Paresh
    Göhring, Gudrun
    Schlegelberger, Brigitte
    Johansson, Bertil
    Hellström-Lindberg, Eva
    List, Alan
    Nilsson, Lars
    Jacobsen, Sten Eirik W.
    Persistent Malignant Stem Cells in del(5q) Myelodysplasia in Remission2010In: New England Journal of Medicine, ISSN 0028-4793, E-ISSN 1533-4406, Vol. 363, no 11, p. 1025-1037Article in journal (Refereed)
    Abstract [en]

    BACKGROUND The in vivo clinical significance of malignant stem cells remains unclear. METHODS Patients who have the 5q deletion (del[5q]) myelodysplastic syndrome (interstitial deletions involving the long arm of chromosome 5) have complete clinical and cytogenetic remissions in response to lenalidomide treatment, but they often have relapse. To determine whether the persistence of rare but distinct malignant stem cells accounts for such relapses, we examined bone marrow specimens obtained from seven patients with the del(5q) myelodysplastic syndrome who became transfusion-independent while receiving lenalidomide treatment and entered cytogenetic remission. RESULTS Virtually all CD34+, CD38+ progenitor cells and stem cells that were positive for CD34 and CD90, with undetectable or low CD38 (CD38-/low), had the 5q deletion before treatment. Although lenalidomide efficiently reduced these progenitors in patients in complete remission, a larger fraction of the minor, quiescent, CD34+, CD38-/low, CD90+ del(5q) stem cells as well as functionally defined del(5q) stem cells remained distinctly resistant to lenalidomide. Over time, lenalidomide resistance developed in most of the patients in partial and complete remission, with recurrence or expansion of the del(5q) clone and clinical and cytogenetic progression. CONCLUSIONS In these patients with the del(5q) myelodysplastic syndrome, we identified rare and phenotypically distinct del(5q) myelodysplastic syndrome stem cells that were also selectively resistant to therapeutic targeting at the time of complete clinical and cytogenetic remission. (Funded by the EuroCancerStemCell Consortium and others.)

  • 31. Wiktorsson, Magnus
    et al.
    Rydén, Tobias
    Lund University.
    Nilsson, Elna
    Bengtsson, Göran
    Modelling the movement of a soil insect2004In: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 231, no 4, p. 497-513Article in journal (Refereed)
    Abstract [en]

    We use a linear autoregressive model to describe the movement of a soil-living insect, Protaphorura armata (Collembola). Models of this kind can be viewed as extensions of a random walk, but unlike a correlated random walk, in which the speed and turning angles are independent, our model identifies and expresses the correlations between the turning angles and a variable speed. Our model uses data in x- and y-coordinates rather than in polar coordinates, which is useful for situations in which the resolution of the observations is limited. The movement of the insect was characterized by (i) looping behaviour due to autocorrelation and cross correlation in the velocity process and (ii) occurrence of periods of inactivity, which we describe with a Poisson random effects model. We also introduce obstacles to the environment to add structural heterogeneity to the movement process. We compare aspects such as loop shape, inter-loop time, holding angles at obstacles, net squared displacement, number, and duration of inactive periods between observed and predicted movement. The comparison demonstrates that our approach is relevant as a starting-point to predict behaviourally complex moving, e.g. systematic searching, in a heterogeneous landscape.

1 - 31 of 31
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