Variable Selection of Generalized Regression Models Based on Maximum Rank Correlation

作     者：Peng-jie DAI Qing-zhao ZHANG Zhi-hua SUN 

作者机构：School of BusinessRenmin University of China Department of MathematicsUniversity of Chinese Academy of Sciences 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2014年第30卷第3期

页      面：833-844页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 070602[理学-大气物理学与大气环境] 0706[理学-大气科学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by National Natural Science Foundation of China(10901162) supported by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China(10XNF073) supported by China Postdoctoral Science Foundation(2014M550799) 

主　　题：maximum rank correlation estimation adaptive LASSO oracle properties generalized regression models. 

摘      要：In this paper, we investigate the variable selection problem of the generalized regression models. To estimate the regression parameter, a procedure combining the rank correlation method and the adaptive lasso technique is developed, which is proved to have oracle properties. A modified IMO （iterative marginal optimization） algorithm which directly aims to maximize the penalized rank correlation function is proposed. The effects of the estimating procedure are illustrated by simulation studies.