Generalised linear mixed model analysis via sequential Monte Carlo sampling

Fan, Y. and Leslie, David S. and Wand, M. P. (2008) Generalised linear mixed model analysis via sequential Monte Carlo sampling. Electronic Journal of Statistics, 2. pp. 916-938. ISSN 1935-7524

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Abstract

We present a sequential Monte Carlo sampler algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but per- forming inference is often extremely difficult, even when using the Bayesian approach combined with Markov chainMonte Carlo (MCMC). The Sequen- tialMonte Carlo sampler (SMC) is a new and generalmethod for producing samples from posterior distributions. In this article we demonstrate use of the SMC method for performing inference for GLMMs. We demonstrate the effectiveness of the method on both simulated and real data, and find that sequential Monte Carlo is a competitive alternative to the available MCMC techniques.

Item Type: Journal Article
Journal or Publication Title: Electronic Journal of Statistics
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 70757
Deposited By: ep_importer_pure
Deposited On: 12 Sep 2014 08:49
Refereed?: Yes
Published?: Published
Last Modified: 29 Jan 2020 07:43
URI: https://eprints.lancs.ac.uk/id/eprint/70757

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