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May 2019 Numerically stable online estimation of variance in particle filters
Jimmy Olsson, Randal Douc
Bernoulli 25(2): 1504-1535 (May 2019). DOI: 10.3150/18-BEJ1028

Abstract

This paper discusses variance estimation in sequential Monte Carlo methods, alternatively termed particle filters. The variance estimator that we propose is a natural modification of that suggested by H.P. Chan and T.L. Lai [Ann. Statist. 41 (2013) 2877–2904], which allows the variance to be estimated in a single run of the particle filter by tracing the genealogical history of the particles. However, due particle lineage degeneracy, the estimator of the mentioned work becomes numerically unstable as the number of sequential particle updates increases. Thus, by tracing only a part of the particles’ genealogy rather than the full one, our estimator gains long-term numerical stability at the cost of a bias. The scope of the genealogical tracing is regulated by a lag, and under mild, easily checked model assumptions, we prove that the bias tends to zero geometrically fast as the lag increases. As confirmed by our numerical results, this allows the bias to be tightly controlled also for moderate particle sample sizes.

Citation

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Jimmy Olsson. Randal Douc. "Numerically stable online estimation of variance in particle filters." Bernoulli 25 (2) 1504 - 1535, May 2019. https://doi.org/10.3150/18-BEJ1028

Information

Received: 1 January 2017; Revised: 1 October 2017; Published: May 2019
First available in Project Euclid: 6 March 2019

zbMATH: 07049414
MathSciNet: MR3920380
Digital Object Identifier: 10.3150/18-BEJ1028

Rights: Copyright © 2019 Bernoulli Society for Mathematical Statistics and Probability

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Vol.25 • No. 2 • May 2019
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