Detection of outliers and influential observations in regression analysis using stochastic prior information

When stochastic prior information is available there can be a considerable improvement in the estimation of β in the usual model Y = Xβ + e . Test statistics and diagnostics are developed to test for outliers and to detect influential observations when the residuals are analysed after these residuals have been computed using stochastic prior information. Applications to Ridge? Generalized Ridge and Stein type estimators and some examples are given.