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Enhanced consistency of the Resampled Convolution Particle Filter

Listed author(s):
  • Vila, Jean-Pierre
Registered author(s):

    Among the convolution particle filters for discrete-time dynamic systems defined by nonlinear state space models, the Resampled Convolution Filter is one of the most efficient, in terms of estimation of the conditional probability density functions (pdf’s) of the state variables and unknown parameters and in terms of implementation. This nonparametric filter is known for its almost sure L1-convergence property. But contrarily to the other convolution filters, its almost sure punctual convergence had not yet been established. This paper is devoted to the proof of this property.

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    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 82 (2012)
    Issue (Month): 4 ()
    Pages: 786-797

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    Handle: RePEc:eee:stapro:v:82:y:2012:i:4:p:786-797
    DOI: 10.1016/j.spl.2012.01.003
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    1. Vila, Jean-Pierre, 2011. "Nonparametric multi-step prediction in nonlinear state space dynamic systems," Statistics & Probability Letters, Elsevier, vol. 81(1), pages 71-76, January.
    2. repec:eee:ecomod:v:206:y:2007:i:3:p:431-438 is not listed on IDEAS
    3. LeGland, François & Oudjane, Nadia, 2003. "A robustification approach to stability and to uniform particle approximation of nonlinear filters: the example of pseudo-mixing signals," Stochastic Processes and their Applications, Elsevier, vol. 106(2), pages 279-316, August.
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