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• W2125432874 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." @default.
• W2125432874 created "2016-06-24" @default.
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• W2125432874 creator A5055067332 @default.
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• W2125432874 date "2014-03-01" @default.
• W2125432874 modified "2023-09-23" @default.
• W2125432874 title "Fast Bayesian model assessment for nonparametric additive regression" @default.
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• W2125432874 doi "https://doi.org/10.1016/j.csda.2013.05.012" @default.
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