Variational inference for marginal longitudinal semiparametric regression

Menictas, M; Wand, MP

Variational inference for marginal longitudinal semiparametric regression

Menictas, M Wand, MP

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Publication Type:: Journal Article
Citation:: Stat, 2013, 2 (1), pp. 61 - 71
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Menictas, M	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Stat, 2013, 2 (1), pp. 61 - 71	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26440
dc.description.abstract	© 2013 John Wiley & Sons Ltd. We derive a variational inference procedure for approximate Bayesian inference in marginal longitudinal semiparametric regression. Fitting and inference is much faster than existing Markov chain Monte Carlo approaches. Numerical studies indicate that the new methodology is very accurate for the class of models under consideration.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110100061
dc.relation.ispartof	Stat	en_US
dc.relation.isbasedon	10.1002/sta4.18	en_US
dc.title	Variational inference for marginal longitudinal semiparametric regression	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	2	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	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

© 2013 John Wiley & Sons Ltd. We derive a variational inference procedure for approximate Bayesian inference in marginal longitudinal semiparametric regression. Fitting and inference is much faster than existing Markov chain Monte Carlo approaches. Numerical studies indicate that the new methodology is very accurate for the class of models under consideration.

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http://hdl.handle.net/10453/26440