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Fast Bayesian model assessment for nonparametric additive regression
, S.McKay Curtis, Subhashis Ghosal
Published in Elsevier BV
2013
Volume: 71
   
Pages: 347 - 358
Abstract

Variable selection techniques for the classical linear regression model have been widely investigated. Variable selection in fully nonparametric and additive regression models has been studied more recently. A Bayesian approach for nonparametric additive regression models is considered, where the functions in the additive model are expanded in a B-spline basis and a multivariate Laplace prior is put on the coefficients. Posterior probabilities of models defined by selection of predictors in the working model are computed, using a Laplace approximation method. The prior times the likelihood is expanded around the posterior mode, which can be identified with the group LASSO, for which a fast computing algorithm exists. Thus Markov chain Monte-Carlo or any other time consuming sampling based methods are completely avoided, leading to quick assessment of various posterior model probabilities. This technique is applied to the high-dimensional situation where the number of parameters exceeds the number of observations. extcopyright 2013 Elsevier B.V. All rights reserved.

About the journal
JournalData powered by TypesetComputational Statistics & Data Analysis
PublisherData powered by TypesetElsevier BV
ISSN0167-9473
Open AccessNo