Multivariate initial sequence estimators in Markov chain Monte Carlo

Ning Dai and Galin L. Jones

Journal of Multivariate Analysis, 2017, vol. 159, issue C, 184-199

Abstract: 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.

Keywords: Markov chain Monte Carlo; Covariance matrix estimation; Central limit theorem; Metropolis–Hastings algorithm; Gibbs sampler (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.jmva.2017.05.009

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