Geometric Ergodicity of Metropolis-Hastings Algorithms for Conditional Simulation in Generalized Linear Mixed Models

O. F. Christensen (), J. Møller () and R. P. Waagepetersen ()
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O. F. Christensen: Aalborg University
J. Møller: Aalborg University
R. P. Waagepetersen: Aalborg University

Methodology and Computing in Applied Probability, 2001, vol. 3, issue 3, 309-327

Abstract: Abstract Conditional simulation is useful in connection with inference and prediction for a generalized linear mixed model. We consider random walk Metropolis and Langevin-Hastings algorithms for simulating the random effects given the observed data, when the joint distribution of the unobserved random effects is multivariate Gaussian. In particular we study the desirable property of geometric ergodicity, which ensures the validity of central limit theorems for Monte Carlo estimates.

Keywords: conditional simulation; generalized linear mixed model; geometric ergodicity; Langevin-Hastings algorithm; Markov chain Monte Carlo; random walk Metropolis algorithm (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1023/A:1013779208892

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