Abstract
The Bayesian analysis of outliers using a non-informative prior for the parameters is non-trivial because models with different numbers of outliers have different dimensions. A quasi-Bayesian approach based on the Akaike's predictive likelihood is proposed for the analysis of regression outliers. It overcomes the dimensionality problem in Bayesian outlier analysis in which the likelihood of the outlier model is compensated by a correction factor adjusted for the number of outliers. The stack loss data set is analysed with satisfactory results.
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