发散维数SICA惩罚Cox回归模型的一种修正BIC调节参数选择器
A MODIFIED BIC TUNING PARAMETER SELECTOR FOR SICA-PENALIZED COX REGRESSION MODELS WITH DIVERGING DIMENSIONALITY

本文研究了发散维数SICA惩罚Cox回归模型的调节参数选择问题，提出了一种修正的BIC调节参数选择器.在一定的正则条件下，证明了方法的模型选择相合性.数值结果表明提出的方法表现要优于GCV准则.
This paper proposes a modifled BIC (Bayesian information criterion) tuning parameter selector for SICA-penalized Cox regression models with a diverging number of covariates. Under some regularity conditions, we prove the model selection consistency of the proposed method. Numerical results show that the proposed method performs better than the GCV (generalized crossvalidation) criterion