Generalised linear mixed model analysis via sequential Monte Carlo sampling

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dc.creatorFan, Y.
dc.creatorLeslie, D. S.
dc.creatorWand, M. P.
dc.date2008-10-07
dc.date.accessioned2026-07-07T10:08:06Z
dc.date.available2026-07-07T10:08:06Z
dc.descriptionWe 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.
dc.descriptionPublished in at http://dx.doi.org/10.1214/07-EJS158 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0810.1163
dc.identifierhttp://arxiv.org/abs/0810.1163
dc.identifierElectronic Journal of Statistics 2008, Vol. 2, 916-938
dc.identifierdoi:10.1214/07-EJS158
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170874
dc.subjectComputation
dc.titleGeneralised linear mixed model analysis via sequential Monte Carlo sampling
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