Convergence rates of kernel density estimates in particle filtering

0506808 - ÚI 2020 RIV NL eng J - Journal Article
Coufal, David
Convergence rates of kernel density estimates in particle filtering.
Statistics & Probability Letters. Roč. 153, October (2019), s. 164-170. ISSN 0167-7152. E-ISSN 1879-2103
Grant - others:GA ČR(CZ) GA16-03708S; OP VVV - Fermilab-CZ(XE) CZ.02.1.01/0.0/0.0/16_013/0001787
Institutional support: RVO:67985807
Keywords : Particle filtering * Kernel density estimates * Convergence rates
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 0.680, year: 2019
Method of publishing: Limited access
http://dx.doi.org/10.1016/j.spl.2019.06.013

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.
Permanent Link: http://hdl.handle.net/11104/0297966