Finite sample performance of Robust Bayesian regression

Michael Smith, Simon Sheather, Robert Kohn

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The finite sample performance of a number of linear regression estimators is investigated in a variety of parametric settings involving outliers. A Bayesian approach is shown to have good overall comparative performance. It is then shown how the same Bayesian methodology can be easily extended to robust nonparametric regression. The Bayesian analysis is carried out using the Gibbs sampler.

Original languageEnglish
Pages (from-to)269-301
Number of pages33
JournalComputational Statistics
Issue number3
StatePublished - 1996


  • Gaussian mixture
  • Gibbs sampling
  • Nonparametric regression
  • Outliers
  • Regression splines

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Mathematics


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