Convergence rates of kernel density estimates in particle filtering

David Coufal

Statistics & Probability Letters, 2019, vol. 153, issue C, 164-170

Abstract: Bounds on convergence rates of kernel density estimates in particle filtering are specified. The kernel density estimates are shown to be efficient for the Sobolev class of filtering densities. The upper bounds are established using Fourier analysis whilst the lower ones rely on tools of information theory.

Keywords: Particle filtering; Kernel density estimates; Convergence rates (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.spl.2019.06.013

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