Adaptive methods for sequential importance sampling with application to state space models

Simple item page
dc.creatorCornebise, Julien
dc.creatorMoulines, Eric
dc.creatorOlsson, Jimmy
dc.date2008-03-01
dc.date2008-08-23
dc.date.accessioned2026-07-07T09:57:50Z
dc.date.available2026-07-07T09:57:50Z
dc.descriptionIn this paper we discuss new adaptive proposal strategies for sequential Monte Carlo algorithms--also known as particle filters--relying on criteria evaluating the quality of the proposed particles. The choice of the proposal distribution is a major concern and can dramatically influence the quality of the estimates. Thus, we show how the long-used coefficient of variation of the weights can be used for estimating the chi-square distance between the target and instrumental distributions of the auxiliary particle filter. As a by-product of this analysis we obtain an auxiliary adjustment multiplier weight type for which this chi-square distance is minimal. Moreover, we establish an empirical estimate of linear complexity of the Kullback-Leibler divergence between the involved distributions. Guided by these results, we discuss adaptive designing of the particle filter proposal distribution and illustrate the methods on a numerical example.
dc.descriptionPreprint of the article to be published in Statistics and Comptuing. 36 pages
dc.identifierhttps://arxiv.org/abs/0803.0054
dc.identifierhttp://arxiv.org/abs/0803.0054
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/167493
dc.subjectComputation
dc.subjectStatistics Theory
dc.subjectMethodology
dc.titleAdaptive methods for sequential importance sampling with application to state space models
dc.typetext

Files

Collections

arXiv