New Robust Variable Selection Methods for Linear Regression Models. (13th January 2014)

• Record Type: Journal Article
• Authors: Chen, Ziqi; Tang, Man‐Lai; Gao, Wei; Shi, Ning‐Zhong
• Is Part Of: Scandinavian journal of statistics. Volume 41:Number 3(2014:Sep.)
• Page Start: 725
• Page End: 741
• Publication Date: 2014-01-13
• Subjects: Statistics -- Periodicals; 310
• DOI: 10.1111/sjos.12057
• Languages: English
• ISSNs: 0303-6898

Abstract

<abstract abstract-type="main" id="sjos12057-abs-0001"> <title>ABSTRACT</title> <p id="sjos12057-para-0001">Motivated by an entropy inequality, we propose for the first time a penalized profile likelihood method for simultaneously selecting significant variables and estimating unknown coefficients in multiple linear regression models in this article. The new method is robust to outliers or errors with heavy tails and works well even for error with infinite variance. Our proposed approach outperforms the adaptive lasso in both theory and practice. It is observed from the simulation studies that (i) the new approach possesses higher probability of correctly selecting the exact model than the least absolute deviation lasso and the adaptively penalized composite quantile regression approach and (ii) exact model selection via our proposed approach is robust regardless of the error distribution. An application to a real dataset is also provided.</p> </abstract>