Theory of Gaussian variational approximation for a Poisson mixed model

Hall, P; Ormerod, JT; Wand, MP

Theory of Gaussian variational approximation for a Poisson mixed model

Hall, P Ormerod, JT Wand, MP

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Publication Type:: Journal Article
Citation:: Statistica Sinica, 2011, 21 (1), pp. 369 - 389
Issue Date:: 2011-01-01

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Field	Value	Language
dc.contributor.author	Hall, P	en_US
dc.contributor.author	Ormerod, JT	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2011-01-01	en_US
dc.identifier.citation	Statistica Sinica, 2011, 21 (1), pp. 369 - 389	en_US
dc.identifier.issn	1017-0405	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17940
dc.description.abstract	Likelihood-based inference for the parameters of generalized linear mixed models is hindered by the presence of intractable integrals. Gaussian variational approximation provides a fast and effective means of approximate inference. We provide some theory for this type of approximation for a simple Poisson mixed model. In particular, we establish consistency at rate m -1/2 +n-1, where m is the number of groups and n is the number of repeated measurements.	en_US
dc.relation.ispartof	Statistica Sinica	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Theory of Gaussian variational approximation for a Poisson mixed model	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	21	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0199 Other Mathematical Sciences	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	21	en_US

Abstract:

Likelihood-based inference for the parameters of generalized linear mixed models is hindered by the presence of intractable integrals. Gaussian variational approximation provides a fast and effective means of approximate inference. We provide some theory for this type of approximation for a simple Poisson mixed model. In particular, we establish consistency at rate m -1/2 +n-1, where m is the number of groups and n is the number of repeated measurements.

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http://hdl.handle.net/10453/17940