Profiled adaptive Elastic-Net procedure for partially linear models with high-dimensional covariates

被引：11
|
作者：
Chen, Baicheng [2 ]
Yu, Yao [1 ]
Zou, Hui [3 ]
Liang, Hua [1 ]
机构：
[1] Univ Rochester, Dept Biostat & Computat Biol, Rochester, NY 14642 USA
[2] Shanghai Univ Finance & Econ, Dept Stat, Shanghai, Peoples R China
[3] Univ Minnesota, Dept Stat, Minneapolis, MN 55455 USA
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2012年 / 142卷 / 07期
关键词：
Adaptive regularization; Elastic-Net; High dimensionality; Model selection; Oracle property; Presmoothing; Semiparametric model; Shrinkage methods; NONCONCAVE PENALIZED LIKELIHOOD; VARIABLE SELECTION; DIVERGING NUMBER; REGRESSION; CONSISTENCY; SHRINKAGE;
D O I：
10.1016/j.jspi.2012.02.035
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：
We study variable selection for partially linear models when the dimension of covariates diverges with the sample size. We combine the ideas of profiling and adaptive Elastic-Net. The resulting procedure has oracle properties and can handle collinearity well. A by-product is the uniform bound for the absolute difference between the profiled and original predictors. We further examine finite sample performance of the proposed procedure by simulation studies and analysis of a labor-market dataset for an illustration. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：1733 / 1745
页数：13
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