A simulation study on classic and robust variable selection in linear regression

Abstract

In linear regression analysis, outliers often have large influence in the variable selection process. The aim of this study is to select the subsets of independent variables, which explain dependent variables in the presence of outliers and possible departures from the normality assumption of the error distribution in robust regression analysis. We compared robust and classical variable selection. Here, as a classics selection criteria we used Cp, AICC and AICF which we proposed. Besides we used Andrews, Huber and Hampel M-estimators in computing of the robust variable selection criteria.