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

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 er...

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Bibliographic Details
Published in:Applied mathematics and computation 2006-04, Vol.175 (2), p.1629-1643
Main Authors: Çetin, Meral, Erar, Aydın
Format: Article
Language:English
Subjects:
Exact sciences and technology
Linear inference, regression
M-estimators
Mallows’ Cp and Akaike criteria
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
Robust regression
Robust variable selection
Sciences and techniques of general use
Statistics
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Summary: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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2005.09.010