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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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.

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Journal Article
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Electronic Journal of Statistics
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12 Sep 2014 08:49
Last Modified:
22 Nov 2022 01:07