Open Access
2011 Functional regression via variational Bayes
Jeff Goldsmith, Matt P. Wand, Ciprian Crainiceanu
Electron. J. Statist. 5: 572-602 (2011). DOI: 10.1214/11-EJS619


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.


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Jeff Goldsmith. Matt P. Wand. Ciprian Crainiceanu. "Functional regression via variational Bayes." Electron. J. Statist. 5 572 - 602, 2011.


Published: 2011
First available in Project Euclid: 15 June 2011

zbMATH: 1274.62200
MathSciNet: MR2813555
Digital Object Identifier: 10.1214/11-EJS619

Keywords: Approximate Bayesian inference , Markov chain Monte Carlo , penalized splines

Rights: Copyright © 2011 The Institute of Mathematical Statistics and the Bernoulli Society

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