Identification of outlying and influential data with biased estimation : A simulation study

Abstract

An important aspect of multiple regression analysis is the identification of out-lying and influential data. There are a number of diagnostic measures and graphical methods available to assist in this identification problem. However, no recommen-dations exist for the use of these diagnostic measures with biased estimators, in par-ticular for those estimators designed to overcome the problem of multicollinearity. We have carried out a simulation study with the ridge regression and the fractional rank estimators to investigate the use of standard cutoffs for some of these diagnos-tic measures. The results indicate that no cutoff can be adopted as standard and the diagnostic values must be interpreted with caution.