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The Loss Rank Criterion for Variable Selection in Linear Regression Analysis

Authors


Minh-Ngoc Tran, Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546.
E-mail: ngoctm@nus.edu.sg

Abstract

Abstract.  Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularization algorithm, a consistent model selection criterion is proposed to select the best one among this preselected set. The approach leads to a fast and efficient procedure for variable selection, especially in high-dimensional settings. Model selection consistency of the suggested criterion is proven when the number of covariates d is fixed. Simulation studies suggest that the criterion still enjoys model selection consistency when d is much larger than the sample size. The simulations also show that our approach for variable selection works surprisingly well in comparison with existing competitors. The method is also applied to a real data set.

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