Multivariate initial sequence estimators in Markov chain Monte Carlo

JOURNAL OF MULTIVARIATE ANALYSIS

卷:159

Article

Dai, Ning¹  Jones, Galin L.¹ 

[1] Univ Minnesota, Sch Stat, Minneapolis, MN 55455 USA

关键词: Markov chain Monte Carlo;    Covariance matrix estimation;    Central limit theorem;    Metropolis-Hastings algorithm;    Gibbs sampler;   

DOI  :  10.1016/j.jmva.2017.05.009

来源: Elsevier

【 摘 要 】

Markov chain Monte Carlo (MCMC) is a simulation method commonly used for estimating expectations with respect to a given distribution. We consider estimating the covariance matrix of the asymptotic multivariate normal distribution of a vector of sample means. Geyer (1992) developed a Monte Carlo error estimation method for estimating a univariate mean. We propose a novel multivariate version of Geyer's method that provides an asymptotically valid estimator for the covariance matrix and results in stable Monte Carlo estimates. The finite sample properties of the proposed method are investigated via simulation experiments. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free