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2018 Weighted batch means estimators in Markov chain Monte Carlo
Ying Liu, James M. Flegal
Electron. J. Statist. 12(2): 3397-3442 (2018). DOI: 10.1214/18-EJS1483

Abstract

This paper proposes a family of weighted batch means variance estimators, which are computationally efficient and can be conveniently applied in practice. The focus is on Markov chain Monte Carlo simulations and estimation of the asymptotic covariance matrix in the Markov chain central limit theorem, where conditions ensuring strong consistency are provided. Finite sample performance is evaluated through auto-regressive, Bayesian spatial-temporal, and Bayesian logistic regression examples, where the new estimators show significant computational gains with a minor sacrifice in variance compared with existing methods.

Citation

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Ying Liu. James M. Flegal. "Weighted batch means estimators in Markov chain Monte Carlo." Electron. J. Statist. 12 (2) 3397 - 3442, 2018. https://doi.org/10.1214/18-EJS1483

Information

Received: 1 July 2017; Published: 2018
First available in Project Euclid: 10 October 2018

zbMATH: 06970008
MathSciNet: MR3862788
Digital Object Identifier: 10.1214/18-EJS1483

Subjects:
Primary: 60J22
Secondary: 62F15

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Vol.12 • No. 2 • 2018
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