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

被引：6

作者：

Çetin, Meral ^{[1
]}

Erar, Aydin

机构：

[1] Hacettepe Univ, Fac Sci, Dept Stat, Ankara, Turkey

[2] Mimar Sinan Fine Art Univ, Fac Sci & Letters, Dept Stat, Istanbul, Turkey

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2006年 / 175卷 / 02期

关键词：

robust variable selection; robust regression; M-estimators; Mallows' Cp and Akaike criteria;

D O I：

10.1016/j.amc.2005.09.010

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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. (c) 2005 Elsevier Inc. All rights reserved.

引用

页码：1629 / 1643

页数：15

共 16 条

[1] NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].