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A simulation study on classic and robust variable selection in linear regression. (English) Zbl 1089.62087

Summary: In linear regression analysis, outliers often have large influence on the variables selection process. The aim of this study is to select 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 compare robust and classical variable selection. Here, as classic selection criteria, we used Cp, AICC and AICF which we proposed. Besides, we used Andrews, Huber and Hampel M-estimators for computing the robust variable selection criteria.

MSC:

62J20 Diagnostics, and linear inference and regression
62J05 Linear regression; mixed models
65C60 Computational problems in statistics (MSC2010)

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References:

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