Variational Bayesian inference for parametric and nonparametric regression with missing data

Faes, C; Ormerod, JT; Wand, MP

Variational Bayesian inference for parametric and nonparametric regression with missing data

Faes, C Ormerod, JT Wand, MP

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Publication Type:: Journal Article
Citation:: Journal of the American Statistical Association, 2011, 106 (495), pp. 959 - 971
Issue Date:: 2011-10-21

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Field	Value	Language
dc.contributor.author	Faes, C	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	2011-10-21	en_US
dc.identifier.citation	Journal of the American Statistical Association, 2011, 106 (495), pp. 959 - 971	en_US
dc.identifier.issn	0162-1459	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17928
dc.description.abstract	Bayesian hierarchical models are attractive structures for conducting regression analyses when the data are subject to missingness. However, the requisite probability calculus is challenging andMonte Carlo methods typically are employed.We develop an alternative approach based on deterministic variational Bayes approximations. Both parametric and nonparametric regression are considered. Attention is restricted to the more challenging case of missing predictor data.We demonstrate that variational Bayes can achieve good accuracy, but with considerably less computational overhead. The main ramification is fast approximate Bayesian inference in parametric and nonparametric regression models with missing data. Supplemental materials accompany the online version of this article. © 2011 American Statistical Association.	en_US
dc.relation.ispartof	Journal of the American Statistical Association	en_US
dc.relation.isbasedon	10.1198/jasa.2011.tm10301	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Variational Bayesian inference for parametric and nonparametric regression with missing data	en_US
dc.type	Journal Article
utslib.citation.volume	495	en_US
utslib.citation.volume	106	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	1603 Demography	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	closed_access
pubs.issue	495	en_US
pubs.publication-status	Published	en_US
pubs.volume	106	en_US

Abstract:

Bayesian hierarchical models are attractive structures for conducting regression analyses when the data are subject to missingness. However, the requisite probability calculus is challenging andMonte Carlo methods typically are employed.We develop an alternative approach based on deterministic variational Bayes approximations. Both parametric and nonparametric regression are considered. Attention is restricted to the more challenging case of missing predictor data.We demonstrate that variational Bayes can achieve good accuracy, but with considerably less computational overhead. The main ramification is fast approximate Bayesian inference in parametric and nonparametric regression models with missing data. Supplemental materials accompany the online version of this article. © 2011 American Statistical Association.

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http://hdl.handle.net/10453/17928