A Robust Look at the Use of Regression Diagnostics

The need to pay special attention to atypical data in regression analysis is generally accepted. First, because of their excessive influence on the regression results. However, also as emphasized by Gray, the unusual data often provide useful information. Thus, even if robust regression techniques offer a remedy to the fitting problem, the need for regression diagnostics remains. Robust techniques lead to powerful remoteness indicators which, unlike the classical measures based on least squares, are themselves insensitive to atypical data. A reexamination of the two examples discussed by Gray shows that these robust indicators advantageously complement the information obtained with classical influence measures.