Mean field variational Bayesian inference for nonparametric regression with measurement error

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...

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Bibliographic Details
Published inComputational statistics & data analysis Vol. 68; pp. 375 - 387
Main Authors Pham, Tung H., Ormerod, John T., Wand, M.P.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2013
Subjects
Accuracy
Bayesian theory
Classical measurement error
Markov chain
Markov chain Monte Carlo
Monte Carlo method
Penalized splines
Variational approximations
Variational approximations
Penalized splines
Classical measurement error
Markov chain Monte Carlo
Online AccessGet full text
ISSN0167-9473
1872-7352
DOI10.1016/j.csda.2013.07.014

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Summary: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.
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ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2013.07.014