Functional regression via variational Bayes

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dc.contributor.author Goldsmith, J en_US
dc.contributor.author Crainiceanu, C. en_US
dc.contributor.author Wand, Matt en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-10-12T03:32:47Z
dc.date.available 2012-10-12T03:32:47Z
dc.date.issued 2011 en_US
dc.identifier 2010005179 en_US
dc.identifier.citation Goldsmith J, Wand Matthew, and Crainiceanu C. 2011, 'Functional regression via variational Bayes', Institute of Mathematical Statistics, vol. 5, pp. 572-602. en_US
dc.identifier.issn 1935-7524 en_US
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. en_US
dc.language en_US
dc.publisher Institute of Mathematical Statistics en_US
dc.relation.isbasedon http://dx.doi.org/10.1214/11-EJS619 en_US
dc.title Functional regression via variational Bayes en_US
dc.parent Electronic Journal of Statistics en_US
dc.journal.volume 5 en_US
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 010400 en_US
dc.personcode 0000069982 en_US
dc.personcode 110509 en_US
dc.personcode 0000070440 en_US
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
dc.description.keywords Approximate Bayesian inference; Markov chain Monte Carlo; penalized splines en_US
dc.staffid en_US


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