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

doi:10.1016/j.jspi.2012.02.035

CORC > 上海财经大学 > 上海财经大学

	Profiled adaptive Elastic-Net procedure for partially linear models with high-dimensional covariates
	Chen, Baicheng 2; Yu, Yao 1; Zou, Hui 3; Liang, Hua 1
刊名	JOURNAL OF STATISTICAL PLANNING AND INFERENCE
	2012-07
卷号	142 期号:7 页码:1733-1745
关键词	Adaptive regularization Elastic-Net High dimensionality Model selection Oracle property Presmoothing Semiparametric model Shrinkage methods
ISSN号	0378-3758
DOI	10.1016/j.jspi.2012.02.035
英文摘要	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.
WOS研究方向	Mathematics
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000304074500009
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/2134]
专题	上海财经大学
通讯作者	Liang, Hua
作者单位	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
推荐引用方式 GB/T 7714	Chen, Baicheng,Yu, Yao,Zou, Hui,et al. Profiled adaptive Elastic-Net procedure for partially linear models with high-dimensional covariates[J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE,2012,142(7):1733-1745.
APA	Chen, Baicheng,Yu, Yao,Zou, Hui,&Liang, Hua.(2012).Profiled adaptive Elastic-Net procedure for partially linear models with high-dimensional covariates.JOURNAL OF STATISTICAL PLANNING AND INFERENCE,142(7),1733-1745.
MLA	Chen, Baicheng,et al."Profiled adaptive Elastic-Net procedure for partially linear models with high-dimensional covariates".JOURNAL OF STATISTICAL PLANNING AND INFERENCE 142.7(2012):1733-1745.