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Generalised linear mixed model analysis via sequential Monte Carlo sampling. (English) Zbl 1320.62178

Summary: 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.

MSC:

62J12 Generalized linear models (logistic models)
62G08 Nonparametric regression and quantile regression
65C05 Monte Carlo methods

Software:

MASS (R); SemiPar
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Full Text: DOI arXiv Euclid

References:

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