Online data processing: comparison of Bayesian regularized particle filters

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dc.creatorCasarin, Roberto
dc.creatorMarin, Jean-Michel
dc.date2008-06-26
dc.date.accessioned2026-07-07T12:19:41Z
dc.date.available2026-07-07T12:19:41Z
dc.descriptionThe aim of this paper is to compare three regularized particle filters in an online data processing context. We carry out the comparison in terms of hidden states filtering and parameters estimation, considering a Bayesian paradigm and a univariate stochastic volatility model. We discuss the use of an improper prior distribution in the initialization of the filtering procedure and show that the regularized Auxiliary Particle Filter (APF) outperforms the regularized Sequential Importance Sampling (SIS) and the regularized Sampling Importance Resampling (SIR).
dc.descriptionSubmitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0806.4242
dc.identifierhttp://arxiv.org/abs/0806.4242
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212843
dc.subjectStatistics Theory
dc.titleOnline data processing: comparison of Bayesian regularized particle filters
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