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Model selection in spline nonparametric regression
Author(s) -
Wood Sally,
Kohn Robert,
Shively Tom,
Jiang Wenxin
Publication year - 2002
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00328
Subject(s) - frequentist inference , nonparametric regression , model selection , gibbs sampling , mathematics , bayesian information criterion , nonparametric statistics , statistics , bayesian linear regression , bayesian probability , regression analysis , smoothness , bayesian inference , computer science , mathematical analysis
A Bayesian approach is presented for model selection in nonparametric regression with Gaussian errors and in binary nonparametric regression. A smoothness prior is assumed for each component of the model and the posterior probabilities of the candidate models are approximated using the Bayesian information criterion. We study the model selection method by simulation and show that it has excellent frequentist properties and gives improved estimates of the regression surface. All the computations are carried out efficiently using the Gibbs sampler.