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Variable Selection via Regularization

Statistical and Numerical Computing

  1. Runze Li1,
  2. Hengjian Cui2

Published Online: 15 JAN 2013

DOI: 10.1002/9780470057339.vnn162

Encyclopedia of Environmetrics

Encyclopedia of Environmetrics

How to Cite

Li, R. and Cui, H. 2013. Variable Selection via Regularization. Encyclopedia of Environmetrics. 6.

Author Information

  1. 1

    The Pennsylvania State University, PA, USA

  2. 2

    Capital Normal University, Beijing, China

Publication History

  1. Published Online: 15 JAN 2013

Abstract

Variable selection has been an active research topic since the 1970s. There are hundreds of publications on variable selection. This entry describes regularization methods for variable selection developed in the recent literature. The regularization methods include classical variable selection criteria such as AIC and BIC and modern variable selection approaches such as LASSO, SCAD, and LARS.

Keywords:

  • AIC;
  • BIC;
  • LASSO;
  • LARS;
  • SCAD