CONSISTENT MODEL SELECTION CRITERIA FOR QUADRATICALLY SUPPORTED RISKS

• 成果类型: Article
• 署名作者: Kim, Yongdai; Jeon, Jong-June
• 署名单位: Seoul National University (SNU); University of Seoul
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/15-AOS1413
• 发表日期: 2016
• 页码: 2467-2496
• 关键词: nonconcave penalized likelihood; tuning parameter selection; quantile regression; variable selection; diverging number

摘要

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 calIed 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.