Robust model selection criteria for robust S and LT S estimators

Abstract

Outliers and multi-collinearity often have large influence in themodel/variable selection process in linear regression analysis. To investigate this combined problem of multi-collinearity and outliers, westudied and compared Liu-type S (liuS-estimators) and Liu-type LeastTrimmed Squares (liuLTS) estimators as robust model selection criteria. Therefore, the main goal of this study is to select subsets of independent variables which explain dependent variables in the presence ofmulti-collinearity, outliers and possible departures from the normalityassumption of the error distribution in regression analysis using thesemodels.