ROBUST VARIABLE SELECTION IN REGRESSION IN THE PRESENCE OF OUTLIERS AND LEVERAGE POINTS

Abstract

SummaryIn linear regression, outliers and leverage points often have large influence in the model selection process. Such cases are downweighted with Mallows‐type weights here, during estimation of submodel parameters by generalised M‐estimation. A robust version of Mallows's Cp (Ronchetti &. Staudte, 1994) is then used to select a variety of submodels which are as informative as the full model. The methodology is illustrated on a new dataset concerning the agglomeration of alumina in Bayer precipitation.