Open Access
2008 Generalised linear mixed model analysis via sequential Monte Carlo sampling
Y. Fan, D.S. Leslie, M.P. Wand
Electron. J. Statist. 2: 916-938 (2008). DOI: 10.1214/07-EJS158


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 performing inference is often extremely difficult, even when using the Bayesian approach combined with Markov chain Monte Carlo (MCMC). The Sequential Monte Carlo sampler (SMC) is a new and general method 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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Y. Fan. D.S. Leslie. M.P. Wand. "Generalised linear mixed model analysis via sequential Monte Carlo sampling." Electron. J. Statist. 2 916 - 938, 2008.


Published: 2008
First available in Project Euclid: 6 October 2008

zbMATH: 1320.62178
MathSciNet: MR2447345
Digital Object Identifier: 10.1214/07-EJS158

Keywords: generalised additive models , longitudinal data analysis , Nonparametric regression , sequential Monte Carlo sampler

Rights: Copyright © 2008 The Institute of Mathematical Statistics and the Bernoulli Society

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