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New Robust Variable Selection Methods for Linear Regression Models

  • Published:2014-01-13 Issue:3 Volume:41 Page:725-741
  • ISSN:0303-6898
  • Container-title:Scandinavian Journal of Statistics
  • language:en
  • Short-container-title:Scand J Statist

Author:

Chen Ziqi1,Tang Man-Lai2,Gao Wei3,Shi Ning-Zhong3

Affiliation:

1. School of Mathematics and Statistics; Central South University

2. Department of Mathematics and Statistics; Hang Seng Management College

3. Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics; Northeast Normal University

Funder

National Nature Science Foundation of China

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference28 articles.

1. Robust parametric classification and variable selection by a minimum distance criterion;Chi;J. Comput. Graph. Statist.

2. Analysis of longitudinal data with semiparametric estimation of covariance function;Fan;J. Amer. Statist. Assoc.,2007

3. Variable selection via nonconcave penalized likelihood and its oracle properties;Fan;J. Amer. Statist. Assoc.,2001

4. Statistical challenges with high dimensionality: feature selection in knowledge discovery;Fan;Proc. Madrid Int. Congress of Mathematicians,2006

5. Nonconcave penalized likelihood with a diverging number of parameters;Fan;Ann. Statist.,2004

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