Genealised linear mixed model analysis via sequential Monte Carlo sampling

Yanan Fan, David S. Leslie, MP Wand

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)

Abstract

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.
Translated title of the contributionGenealised linear mixed model analysis via sequential Monte Carlo sampling
Original languageEnglish
Pages (from-to)916 - 938
Number of pages23
JournalElectronic Journal of Statistics
Volume2
DOIs
Publication statusPublished - Jan 2008

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