Generalised linear mixed model analysis via sequential Monte Carlo sampling

Fan, Y; Leslie, DS; Wand, MP

Generalised linear mixed model analysis via sequential Monte Carlo sampling

Fan, Y Leslie, DS Wand, MP

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Publication Type:: Journal Article
Citation:: Electronic Journal of Statistics, 2008, 2 pp. 916 - 938
Issue Date:: 2008-01-01

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Field	Value	Language
dc.contributor.author	Fan, Y	en_US
dc.contributor.author	Leslie, DS	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2008-01-01	en_US
dc.identifier.citation	Electronic Journal of Statistics, 2008, 2 pp. 916 - 938	en_US
dc.identifier.issn	1935-7524	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41309
dc.identifier.uri	http://hdl.handle.net/10453/17538
dc.description.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 chainMonte Carlo (MCMC). The SequentialMonte Carlo sampler (SMC) is a new and generalmethod for producing samples from posterior distributions. In thisarticle 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. © 2008, Institute of Mathematical Statistics. All rights reserved.	en_US
dc.relation.ispartof	Electronic Journal of Statistics	en_US
dc.relation.isbasedon	10.1214/07-EJS158	en_US
dc.title	Generalised linear mixed model analysis via sequential Monte Carlo sampling	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.for	0104 Statistics	en_US
dc.location.activity	Marmara University, Istanbul
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

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 chainMonte Carlo (MCMC). The SequentialMonte Carlo sampler (SMC) is a new and generalmethod for producing samples from posterior distributions. In thisarticle 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. © 2008, Institute of Mathematical Statistics. All rights reserved.

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http://hdl.handle.net/10453/17538