Finite sample performance of Robust Bayesian regression

Abstract

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.