Robust model selection criteria for robust S and LTS estimators

Year 2016, Volume: 45 Issue: 1, 153 - 164, 01.02.2016

Meral Çetin

Abstract

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 (liuS-estimators) and Liu-type Least
Trimmed 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 of
multi-collinearity, outliers and possible departures from the normality
assumption of the error distribution in regression analysis using these
models. 

Keywords

Liu-estimator, robust-Liu estimator, M-estimator, robust cp, robust Tp, robust model selection

References

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