Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models

Simple item page
dc.creatorOlsson, Jimmy
dc.creatorCappé, Olivier
dc.creatorDouc, Randal
dc.creatorMoulines, Eric
dc.date2006-09-19
dc.date2008-03-06
dc.date.accessioned2026-07-07T09:25:00Z
dc.date.available2026-07-07T09:25:00Z
dc.descriptionThis paper concerns the use of sequential Monte Carlo methods (SMC) for smoothing in general state space models. A well-known problem when applying the standard SMC technique in the smoothing mode is that the resampling mechanism introduces degeneracy of the approximation in the path space. However, when performing maximum likelihood estimation via the EM algorithm, all functionals involved are of additive form for a large subclass of models. To cope with the problem in this case, a modification of the standard method (based on a technique proposed by Kitagawa and Sato) is suggested. Our algorithm relies on forgetting properties of the filtering dynamics and the quality of the estimates produced is investigated, both theoretically and via simulations.
dc.descriptionPublished in at http://dx.doi.org/10.3150/07-BEJ6150 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
dc.identifierhttps://arxiv.org/abs/math/0609514
dc.identifierhttp://arxiv.org/abs/math/0609514
dc.identifierBernoulli 14, 1 (2008) 155-179
dc.identifierdoi:10.3150/07-BEJ6150
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/156268
dc.subjectStatistics Theory
dc.titleSequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models
dc.typetext

Files

Collections

arXiv