Improving the performance of the two-stage sampling particle filter: A statistical perspective

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Abstract

In this paper we study asymptotic properties of weighted samples produced by the two-stage sampling (TSS) particle filter, which is a generalization of the auxiliary particle filter proposed by [1]. Besides establishing a central limit theorem (CLT) for the particle estimator of the smoothing measure, we also present bounds on the Lp error and bias of the same for a nite particle sample size. The main contribution of this article, being based on [2], is the identification of first-stage importance weights for which the increase of asymptotic variance of the CLT at a single iteration of the algorithm is minimal. Finally, we let a simple numerical example illustrate our findings.

Original languageEnglish
Title of host publication2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings
Pages284-288
Number of pages5
DOIs
Publication statusPublished - 1 Dec 2007
Event2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007 - Madison, WI, United States
Duration: 26 Aug 200729 Aug 2007

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Conference

Conference2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007
Country/TerritoryUnited States
CityMadison, WI
Period26/08/0729/08/07

Keywords

  • Auxiliary particle filter
  • CLT
  • Sequential Monte Carlo
  • State space models
  • Two-stage sampling

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