On the oracle property of adaptive group Lasso in high-dimensional linear models

被引：16
|
作者：
Zhang, Caiya [1 ]
Xiang, Yanbiao [2 ]
机构：
[1] Zhejiang Univ City Coll, Hangzhou 310003, Zhejiang, Peoples R China
[2] Peoples Bank China, Hangzhou Branch, Hangzhou, Zhejiang, Peoples R China
来源：
STATISTICAL PAPERS | 2016年 / 57卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Adaptive group Lasso; Oracle property; High dimensionality; Regression model; NONCONCAVE PENALIZED LIKELIHOOD; ASYMPTOTIC PROPERTIES; VARIABLE SELECTION; REGRESSION-MODELS; DIVERGING NUMBER; ELASTIC-NET; REGULARIZATION; CONSISTENCY; PARAMETERS;
D O I：
10.1007/s00362-015-0684-0
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this paper, we consider the adaptive group Lasso in high-dimensional linear regression. Some extensions have been done with other fitting procedures, such as adaptive Lasso, nonconcave penalized likelihood and adaptive elastic-net. Under appropriate conditions, we establish the consistency and asymptotic normality, which means that the adaptive group Lasso shares the oracle property in high-dimensional linear regression when the number of group variables diverges with the sample size.
引用
收藏
页码：249 / 265
页数：17
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