Annals of Statistics

Negative association, ordering and convergence of resampling methods

Mathieu Gerber, Nicolas Chopin, and Nick Whiteley

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Abstract

We study convergence and convergence rates for resampling schemes. Our first main result is a general consistency theorem based on the notion of negative association, which is applied to establish the almost sure weak convergence of measures output from Kitagawa’s [J. Comput. Graph. Statist. 5 (1996) 1–25] stratified resampling method. Carpenter, Ckiffird and Fearnhead’s [IEE Proc. Radar Sonar Navig. 146 (1999) 2–7] systematic resampling method is similar in structure but can fail to converge depending on the order of the input samples. We introduce a new resampling algorithm based on a stochastic rounding technique of [In 42nd IEEE Symposium on Foundations of Computer Science (Las Vegas, NV, 2001) (2001) 588–597 IEEE Computer Soc.], which shares some attractive properties of systematic resampling, but which exhibits negative association and, therefore, converges irrespective of the order of the input samples. We confirm a conjecture made by [J. Comput. Graph. Statist. 5 (1996) 1–25] that ordering input samples by their states in $\mathbb{R}$ yields a faster rate of convergence; we establish that when particles are ordered using the Hilbert curve in $\mathbb{R}^{d}$, the variance of the resampling error is ${\scriptstyle\mathcal{O}}(N^{-(1+1/d)})$ under mild conditions, where $N$ is the number of particles. We use these results to establish asymptotic properties of particle algorithms based on resampling schemes that differ from multinomial resampling.

Article information

Source
Ann. Statist., Volume 47, Number 4 (2019), 2236-2260.

Dates
Received: August 2017
Revised: June 2018
First available in Project Euclid: 21 May 2019

Permanent link to this document
https://projecteuclid.org/euclid.aos/1558425644

Digital Object Identifier
doi:10.1214/18-AOS1746

Mathematical Reviews number (MathSciNet)
MR3953450

Zentralblatt MATH identifier
07082285

Subjects
Primary: 62G09: Resampling methods 62M20: Prediction [See also 60G25]; filtering [See also 60G35, 93E10, 93E11]
Secondary: 60G35: Signal detection and filtering [See also 62M20, 93E10, 93E11, 94Axx]

Keywords
Negative association resampling methods particle filtering

Citation

Gerber, Mathieu; Chopin, Nicolas; Whiteley, Nick. Negative association, ordering and convergence of resampling methods. Ann. Statist. 47 (2019), no. 4, 2236--2260. doi:10.1214/18-AOS1746. https://projecteuclid.org/euclid.aos/1558425644


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Supplemental materials

  • Supplementary material: Proofs. This supplement contains all the proofs.