The Loss Rank Criterion for Variable Selection in Linear Regression Analysis

The Loss Rank Criterion for Variable Selection in Linear Regression Analysis

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Article ID: iaor201112563
Volume: 38
Issue: 3
Start Page Number: 466
End Page Number: 479
Publication Date: Sep 2011
Journal: Scandinavian Journal of Statistics
Authors:
Keywords: datamining, simulation, simulation: applications, simulation: analysis, statistics: decision
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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