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1 CMS - Center for Mathematical Sciences 2 LTCI - Laboratoire Traitement et Communication de l-Information 3 CMAP - Centre de Mathématiques Appliquées

Abstract : This 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.

keyword : EM algorithm particle filter sequential Monte Carlo state space models stochastic volatility model





Autor: Jimmy Olsson - Olivier Cappé - Randal Douc - Eric Moulines -

Fuente: https://hal.archives-ouvertes.fr/



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