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Abstract Details

Activity Number: 466
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #304231
Title: Variable Selection via Subtle Uprooting
Author(s): Xiaogang Su*+
Companies: The University of Alabama at Birmingham
Address: Center for Nursing Research, Birmingham, AL, 35294-1210, United States
Keywords: Variable Selection ; Non-Convex Penalty ; L1 Regularization ; AIC ; BIC

A simple variable selection method termed "subtle uprooting" is put forward for linear regression. In this method, variable selection is formulated as a non-convex programming problem in a penalized function form. The significance of this method is that tuning the penalty parameter and hence computing the whole regularization path are avoided. The solution of the problem can be conveniently obtained via coordinate descent (also aided with some global optimization routines). Both theoretical insight and empirical evidence are provided to support and illustrate how promising the proposed method is.

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