Outlier detection by regression diagnostics based on robust parameter estimates

Abstract

In this article, robust versions of some of the frequently used diagnos- tics are considered to identify outliers instead of the diagnostics based on the least square method. These diagnostics are Cook’s distance, the Welsch-Kuh distance and the Hadi measure. A simulation study is per- formed to compare the performance of the classical diagnostics with the proposed diagnostics based on robust M estimation to identify outliers.