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Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models

Christensen, O. F. and Møller, J. and Waagepetersen, R. P. (2001) Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models. Methodology and computing in applied probability, 3 (3). pp. 309-327. ISSN 1387-5841

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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.

Item Type: Article
Journal or Publication Title: Methodology and computing in applied probability
Uncontrolled Keywords: conditional simulation - generalized linear mixed model - geometric ergodicity - Langevin-Hastings algorithm - Markov chain Monte Carlo - random walk Metropolis algorithm
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 19299
Deposited By: ep_ss_importer
Deposited On: 18 Nov 2008 15:27
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 15:28
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/19299

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