Functional regression via variational Bayes.

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dc.contributor.author Goldsmith, J
dc.contributor.author Wand, MP
dc.contributor.author Crainiceanu, C
dc.date.accessioned 2012-10-12T03:32:47Z
dc.date.issued 2011-01
dc.identifier.citation Electronic journal of statistics, 2011, 5 pp. 572 - 602
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/17927
dc.description.abstract We introduce variational Bayes methods for fast approximate inference in functional regression analysis. Both the standard cross-sectional and the increasingly common longitudinal settings are treated. The methodology allows Bayesian functional regression analyses to be conducted without the computational overhead of Monte Carlo methods. Confidence intervals of the model parameters are obtained both using the approximate variational approach and nonparametric resampling of clusters. The latter approach is possible because our variational Bayes functional regression approach is computationally efficient. A simulation study indicates that variational Bayes is highly accurate in estimating the parameters of interest and in approximating the Markov chain Monte Carlo-sampled joint posterior distribution of the model parameters. The methods apply generally, but are motivated by a longitudinal neuroimaging study of multiple sclerosis patients. Code used in simulations is made available as a web-supplement.
dc.format Print
dc.language eng
dc.relation.isbasedon 10.1214/11-ejs619
dc.title Functional regression via variational Bayes.
dc.type Journal Article
dc.parent Electronic journal of statistics
dc.journal.volume 5
dc.journal.number en_US
dc.publocation United States en_US
dc.identifier.startpage 572 en_US
dc.identifier.endpage 602 en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 0104 Statistics
dc.personcode 110509
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Science
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
pubs.consider-herdc true
utslib.collection.history School of Mathematical Sciences (ID: 340)
utslib.collection.history General (ID: 2)


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