Variable selection of generalized regression models based on maximum rank correlation |
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Authors: | Peng-jie Dai Qing-zhao Zhang Zhi-hua Sun |
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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
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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. |
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Keywords: | maximum rank correlation estimation adaptive LASSO oracle properties generalized regression models |
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