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Online data processing: comparison of Bayesian regularized particle filters

Abstract : The 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 (R-APF) outperforms the Regularized Sequential Importance Sampling (R-SIS) and the Regularized Sampling Importance Resampling (R-SIR).
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Contributor : Jean-Michel Marin Connect in order to contact the contributor
Submitted on : Tuesday, March 4, 2008 - 11:52:18 PM
Last modification on : Wednesday, April 20, 2022 - 3:37:42 AM
Long-term archiving on: : Thursday, September 23, 2010 - 5:17:07 PM


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Roberto Casarin, Jean-Michel Marin. Online data processing: comparison of Bayesian regularized particle filters. Electronic Journal of Statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2009, 3, pp.239-258. ⟨10.1214/08-EJS256⟩. ⟨inria-00138007v3⟩



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