Model selection in spline nonparametric regression

Sally Wood, Robert Kohn, Tom Shively, Wenxin Jiang

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)119-139
Number of pages21
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number1
StatePublished - 2002


  • Bayesian analysis
  • Bayesian information criterion
  • Binary regression
  • Gibbs sampler
  • Thin plate splines
  • Variable selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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