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: | Tran Minh-Ngoc |
Keywords: | datamining, simulation, simulation: applications, simulation: analysis, statistics: decision |
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.