Journal
ELECTRONIC JOURNAL OF STATISTICS
Volume 2, Issue -, Pages 916-938Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/07-EJS158
Keywords
generalised additive models; longitudinal data analysis; nonparametric regression; sequential Monte Carlo sampler
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Funding
- Australian Research Council [DP0877432, DP0877055]
- Nuffield Foundation [NAL/00803/G]
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We present 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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