Comment on‘Review of sparse sufficient dimension reduction’

作     者：Michael Declan Power Yuexiao Dong 

作者机构：Department of Statistical ScienceTemple UniversityPhiladelphiaPAUSA 

出 版 物：《Statistical Theory and Related Fields》 (统计理论及其应用（英文）)

年 卷 期：2020年第4卷第2期

页      面：149-150页

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] 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] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：dimension sparse SDR 

摘      要：We congratulate the authors on a very interesting overview of sparse sufficient dimension reduction(SDR).Sparse SDR methods are discussed in both the classical np setting as well as the high-dimensional pn *** topics such as model-free variable selection and variable screening are also discussed in a most logical fashion.