Bayesian approach to outlier detection in multivariate normal samples and
linear models
by Alexandre Varbanov
Technical Report No. 614
School of Statistics
University of Minnesota
June 26, 1996
Abstract
Chaloner and Brant (1988) propose a Bayesian method for identifying outliers
in univariate linear models. This paper presents an approach generalizing
their idea to multivariate normal samples and multivariate linear models.
The posterior distribution of the squared norm of the realized errors is
used for outlier identification. Bayes factors are used for examining
whether or not an observation is an outlier.
Some key words: Bayes factors; Multivariate linear models; Outlier
detection; Posterior distribution.
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