Bernoulli

  • Bernoulli
  • Volume 25, Number 2 (2019), 1504-1535.

Numerically stable online estimation of variance in particle filters

Jimmy Olsson and Randal Douc

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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.

Article information

Source
Bernoulli, Volume 25, Number 2 (2019), 1504-1535.

Dates
Received: January 2017
Revised: October 2017
First available in Project Euclid: 6 March 2019

Permanent link to this document
https://projecteuclid.org/euclid.bj/1551862858

Digital Object Identifier
doi:10.3150/18-BEJ1028

Mathematical Reviews number (MathSciNet)
MR3920380

Zentralblatt MATH identifier
07049414

Keywords
asymptotic variance Feynman–Kac models hidden Markov models particle filters sequential Monte Carlo methods state-space models variance estimation

Citation

Olsson, Jimmy; Douc, Randal. Numerically stable online estimation of variance in particle filters. Bernoulli 25 (2019), no. 2, 1504--1535. doi:10.3150/18-BEJ1028. https://projecteuclid.org/euclid.bj/1551862858


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