Variable selection of generalized regression models based on maximum rank correlation |
| |
| Authors: | Peng-jie Dai Qing-zhao Zhang Zhi-hua Sun |
| |
| Institution: | 1. School of Business, Renmin University of China, Beijing, 100872, China
2. Department of Mathematics, University of Chinese Academy of Sciences, Beijing, 100049, China
|
| |
| Abstract: | 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. |
| |
| Keywords: | maximum rank correlation estimation adaptive LASSO oracle properties generalized regression models |
| 本文献已被 CNKI 维普 SpringerLink 等数据库收录！ |
|
