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December 2016 Consistent model selection criteria for quadratically supported risks
Yongdai Kim, Jong-June Jeon
Ann. Statist. 44(6): 2467-2496 (December 2016). DOI: 10.1214/15-AOS1413


In this paper, we study asymptotic properties of model selection criteria for high-dimensional regression models where the number of covariates is much larger than the sample size. In particular, we consider a class of loss functions called the class of quadratically supported risks which is large enough to include the quadratic loss, Huber loss, quantile loss and logistic loss. We provide sufficient conditions for the model selection criteria, which are applicable to the class of quadratically supported risks. Our results extend most previous sufficient conditions for model selection consistency. In addition, sufficient conditions for pathconsistency of the Lasso and nonconvex penalized estimators are presented. Here, pathconsistency means that the probability of the solution path that includes the true model converges to 1. Pathconsistency makes it practically feasible to apply consistent model selection criteria to high-dimensional data. The data-adaptive model selection procedure is proposed which is selection consistent and performs well for finite samples. Results of simulation studies as well as real data analysis are presented to compare the finite sample performances of the proposed data-adaptive model selection criterion with other competitors.


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Yongdai Kim. Jong-June Jeon. "Consistent model selection criteria for quadratically supported risks." Ann. Statist. 44 (6) 2467 - 2496, December 2016.


Received: 1 April 2015; Revised: 1 November 2015; Published: December 2016
First available in Project Euclid: 23 November 2016

zbMATH: 1365.60030
MathSciNet: MR3576551
Digital Object Identifier: 10.1214/15-AOS1413

Primary: 60K35

Keywords: Generalized information criteria , high dimension , Model selection , quadratically supported risks , selection consistency

Rights: Copyright © 2016 Institute of Mathematical Statistics


Vol.44 • No. 6 • December 2016
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