Robust model selection criteria for robust S and LTS estimators

Outliers and multi-collinearity often have large influence in the model/variable selection process in linear regression analysis. To investigate this combined problem of multi-collinearity and outliers, we studied and compared Liu-type S insert ignore into journalissuearticles values(liuS-estimators); and Liu-type Least Trimmed Squares insert ignore into journalissuearticles values(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 of multi-collinearity, outliers and possible departures from the normality assumption of the error distribution in regression analysis using these models.