Adaptive group Lasso for high-dimensional generalized linear models

被引：1
|
作者：
Mingqiu Wang
Guo-Liang Tian
机构：
[1] Qufu Normal University,School of Statistics
[2] Southern University of Science and Technology,Department of Mathematics
来源：
Statistical Papers | 2019年 / 60卷
关键词：
Generalized linear models; Group selection; High-dimensional data; Oracle property; 62F12; 62J12;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Variable selection in a grouped manner is an attractive method since it respects the grouping structure in the data. In this paper, we study the adaptive group Lasso in the frame of high-dimensional generalized linear models. Both the number of groups diverging with the sample size and the number of groups exceeding the sample size are considered. The selection consistency and asymptotic normality of the adaptive group Lasso are established under appropriate conditions. Simulation studies confirm superior performances of the adaptive group Lasso.
引用
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页码：1469 / 1486
页数：17
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