A Bayesian approach to outlier detection and residual analysis

Abstract

An approach to detecting outliers in a linear model is developed. An outlier is defined to be an observation with a large random error, generated by the linear model under consideration. Outliers are detected by examining the posterior distribution of the random errors. An augmented residual plot is also suggested as a graphical aid in finding outliers.