Authors: Gather, Ursula
Guddat, Charlotte
Kuhnt, Sonja
Title: MCD-RoSIS - A robust procedure for variable selection
Language (ISO): en
Abstract: Consider the task of estimating a regression function for describing the relationship between a response and a vector of p predictors. Often only a small subset of all given candidate predictors actually effects the response, while the rest might inhibit the analysis. Procedures for variable selection aim to identify the true predictors. A method for variable selection when the dimension p of the regressor space is much larger than the sample size n is SIS — Sure Independence Screening — recently proposed by Fan and Lv (2008). The number of predictors is to be reduced to a value less than the number of observations before conducting the regression analysis. As SIS is based on nonrobust estimators, outliers in the data might lead to the elimination of true predictors. Hence, Gather and Guddat (2008) propose a robustified version of SIS called RoSIS which is based on robust estimators. Here, we give a modification of RoSIS by using the MCD estimator in the new algorithm. The new procedure MCD-RoSIS leads to better results, especially under collinearity. In a simulation study we compare the performance of SIS, RoSIS and MCD-RoSIS w.r.t. their robustness against different types of data contamination as well as different degrees of collinearity.
Subject Headings: Dimension reduction
Outlier
Regression
Robust estimation
Variable selection
URI: http://hdl.handle.net/2003/26672
http://dx.doi.org/10.17877/DE290R-12783
Issue Date: 2010-02-08T14:40:40Z
Appears in Collections:Sonderforschungsbereich (SFB) 823

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