Variational inference for count response semiparametric regression

Luts, J; Wand, MP

Variational inference for count response semiparametric regression

Luts, J Wand, MP

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Publication Type:: Journal Article
Citation:: Bayesian Analysis, 2015, 10 (4), pp. 991 - 1023
Issue Date:: 2015-12-01

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Field	Value	Language
dc.contributor.author	Luts, J	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2015-12-01	en_US
dc.identifier.citation	Bayesian Analysis, 2015, 10 (4), pp. 991 - 1023	en_US
dc.identifier.issn	1936-0975	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41193
dc.description.abstract	© 2015 International Society for Bayesian Analysis. Fast variational approximate algorithms are developed for Bayesian semiparametric regression when the response variable is a count, i.e., a nonnegative integer. We treat both the Poisson and Negative Binomial families as models for the response variable. Our approach utilizes recently developed methodology known as non-conjugate variational message passing. For concreteness, we focus on generalized additive mixed models, although our variational approximation approach extends to a wide class of semiparametric regression models such as those containing interactions and elaborate random effect structure.	en_US
dc.relation.ispartof	Bayesian Analysis	en_US
dc.relation.isbasedon	10.1214/14-BA932	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Variational inference for count response semiparametric regression	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	10	en_US
utslib.for	0104 Statistics	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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

© 2015 International Society for Bayesian Analysis. Fast variational approximate algorithms are developed for Bayesian semiparametric regression when the response variable is a count, i.e., a nonnegative integer. We treat both the Poisson and Negative Binomial families as models for the response variable. Our approach utilizes recently developed methodology known as non-conjugate variational message passing. For concreteness, we focus on generalized additive mixed models, although our variational approximation approach extends to a wide class of semiparametric regression models such as those containing interactions and elaborate random effect structure.

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http://hdl.handle.net/10453/41193