Joint feature screening for ultra-high-dimensional sparse additive hazards model by the sparsity-restricted pseudo-score estimator

doi:10.1007/s10463-018-0675-8

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	Joint feature screening for ultra-high-dimensional sparse additive hazards model by the sparsity-restricted pseudo-score estimator
	Chen, Xiaolin 1; Liu, Yi 2; Wang, Qihua3,4
	2019-10-01
发表期刊	ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
ISSN	0020-3157
卷号	71 期号:5 页码:1007-1031
摘要	Due to the coexistence of ultra-high dimensionality and right censoring, it is very challenging to develop feature screening procedure for ultra-high-dimensional survival data. In this paper, we propose a joint screening approach for the sparse additive hazards model with ultra-high-dimensional features. Our proposed screening is based on a sparsity-restricted pseudo-score estimator which could be obtained effectively through the iterative hard-thresholding algorithm. We establish the sure screening property of the proposed procedure theoretically under rather mild assumptions. Extensive simulation studies verify its improvements over the main existing screening approaches for ultra-high-dimensional survival data. Finally, the proposed screening method is illustrated by dataset from a breast cancer study.
关键词	Additive hazards model Joint feature screening Iterative hard-thresholding algorithm Sure screening property
DOI	10.1007/s10463-018-0675-8
语种	英语
资助项目	National Natural Science Foundation of China[11501573] ; National Natural Science Foundation of China[11326184] ; National Natural Science Foundation of China[11771250] ; National Natural Science Foundation of China[11171331] ; National Natural Science Foundation of China[11331011] ; National Natural Science Foundation of China[61621003] ; National Social Science Foundation of China[17BTJ019] ; Fundamental Research Funds for the Central Universities[17CX02035A] ; Key Lab of Random Complex Structure and Data Science, CAS ; Zhejiang Gongshang University
WOS研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:000481796100001
出版者	SPRINGER HEIDELBERG
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/35372
专题	应用数学研究所
通讯作者	Wang, Qihua
作者单位	1.Qufu Normal Univ, Sch Stat, Qufu 273165, Peoples R China 2.China Univ Petr East China, Coll Sci, Qingdao 266580, Shandong, Peoples R China 3.Zhejiang Gongshang Univ, Dept Math & Stat, Hangzhou 310018, Zhejiang, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Chen, Xiaolin,Liu, Yi,Wang, Qihua. Joint feature screening for ultra-high-dimensional sparse additive hazards model by the sparsity-restricted pseudo-score estimator[J]. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS,2019,71(5):1007-1031.
APA	Chen, Xiaolin,Liu, Yi,&Wang, Qihua.(2019).Joint feature screening for ultra-high-dimensional sparse additive hazards model by the sparsity-restricted pseudo-score estimator.ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS,71(5),1007-1031.
MLA	Chen, Xiaolin,et al."Joint feature screening for ultra-high-dimensional sparse additive hazards model by the sparsity-restricted pseudo-score estimator".ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 71.5(2019):1007-1031.