2006/09/30 by Jimmy Olsson, Olivier Cappé, Randal Douc +2 · 4 citations
Computer Science · Economics, Econometrics and Finance · Mathematics · Social Sciences · #Financial Risk and Volatility Modeling #Insurance, Mortality, Demography, Risk Management #Target Tracking and Data Fusion in Sensor Networks #math.ST #stat.TH
paper · pdf · doi:10.3150/07-bej6150
published as Bernoulli 14, 1 (2008) 155-179 · Published 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)
openalex publication_date 2008/02/01 · arxiv created 2008/03/06 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.