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Nyquist, Pierre
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Mathematical Statistics
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Probability Theory and Statistics
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Large deviations for weighted empirical measures and processes arising in importance samplingPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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2013 (English)Licentiate thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Stockholm: KTH Royal Institute of Technology, 2013. , p. iv, 20
##### Series

Trita-MAT, ISSN 1401-2286 ; 13:01
##### National Category

Probability Theory and Statistics
##### Identifiers

URN: urn:nbn:se:kth:diva-117810ISBN: 978-91-7501-644-3 (print)OAI: oai:DiVA.org:kth-117810DiVA, id: diva2:603126
##### Presentation

2013-02-25, 3721, Lindstedtsvägen 25, KTH, Stockholm, 15:15 (English)
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##### Note

##### List of papers

This thesis consists of two papers related to large deviation results associated with importance sampling algorithms. As the need for efficient computational methods increases, so does the need for theoretical analysis of simulation algorithms. This thesis is mainly concerned with algorithms using importance sampling. Both papers make theoretical contributions to the development of a new approach for analyzing efficiency of importance sampling algorithms by means of large deviation theory.

In the first paper of the thesis, the efficiency of an importance sampling algorithm is studied using a large deviation result for the sequence of weighted empirical measures that represent the output of the algorithm. The main result is stated in terms of the Laplace principle for the weighted empirical measure arising in importance sampling and it can be viewed as a weighted version of Sanov's theorem. This result is used to quantify the performance of an importance sampling algorithm over a collection of subsets of a given target set as well as quantile estimates. The method of proof is the weak convergence approach to large deviations developed by Dupuis and Ellis.

The second paper studies moderate deviations of the empirical process analogue of the weighted empirical measure arising in importance sampling. Using moderate deviation results for empirical processes the moderate deviation principle is proved for weighted empirical processes that arise in importance sampling. This result can be thought of as the empirical process analogue of the main result of the first paper and the proof is established using standard techniques for empirical processes and Banach space valued random variables. The moderate deviation principle for the importance sampling estimator of the tail of a distribution follows as a corollary. From this, moderate deviation results are established for importance sampling estimators of two risk measures: The quantile process and Expected Shortfall. The results are proved using a delta method for large deviations established by Gao and Zhao (2011) together with more classical results from the theory of large deviations.

The thesis begins with an informal discussion of stochastic simulation, in particular importance sampling, followed by short mathematical introductions to large deviations and importance sampling.

QC 20130205

Available from: 2013-02-05 Created: 2013-02-05 Last updated: 2022-06-24Bibliographically approved1. Moderate deviation principles for importance sampling estimators of risk measures$(function(){PrimeFaces.cw("OverlayPanel","overlay603119",{id:"formSmash:j_idt546:0:j_idt551",widgetVar:"overlay603119",target:"formSmash:j_idt546:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Large deviations for weighted empirical measures arising in importance sampling$(function(){PrimeFaces.cw("OverlayPanel","overlay603116",{id:"formSmash:j_idt546:1:j_idt551",widgetVar:"overlay603116",target:"formSmash:j_idt546:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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