Mean field variational Bayesian inference for nonparametric regression with measurement error

Pham, TH; Ormerod, JT; Wand, MP

Mean field variational Bayesian inference for nonparametric regression with measurement error

Pham, TH Ormerod, JT Wand, MP

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Publication Type:: Journal Article
Citation:: Computational Statistics and Data Analysis, 2013, 68 pp. 375 - 387
Issue Date:: 2013-08-26

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Field	Value	Language
dc.contributor.author	Pham, TH	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	2013-08-26	en_US
dc.identifier.citation	Computational Statistics and Data Analysis, 2013, 68 pp. 375 - 387	en_US
dc.identifier.issn	0167-9473	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26437
dc.description.abstract	A fast mean field variational Bayes (MFVB) approach to nonparametric regression when the predictors are subject to classical measurement error is investigated. It is shown that the use of such technology to the measurement error setting achieves reasonable accuracy. In tandem with the methodological development, a customized Markov chain Monte Carlo method is developed to facilitate the evaluation of accuracy of the MFVB method. © 2013 Published by Elsevier B.V. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110100061
dc.relation.ispartof	Computational Statistics and Data Analysis	en_US
dc.relation.isbasedon	10.1016/j.csda.2013.07.014	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Mean field variational Bayesian inference for nonparametric regression with measurement error	en_US
dc.type	Journal Article
utslib.citation.volume	68	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	1403 Econometrics	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.publication-status	Published	en_US
pubs.volume	68	en_US

Abstract:

A fast mean field variational Bayes (MFVB) approach to nonparametric regression when the predictors are subject to classical measurement error is investigated. It is shown that the use of such technology to the measurement error setting achieves reasonable accuracy. In tandem with the methodological development, a customized Markov chain Monte Carlo method is developed to facilitate the evaluation of accuracy of the MFVB method. © 2013 Published by Elsevier B.V. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26437