Identification of local sparsity and variable selection for varying coefficient additive hazards models

doi:10.1016/j.csda.2018.04.003

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 应用数学研究所

	Identification of local sparsity and variable selection for varying coefficient additive hazards models
	Qu, Lianqiang 1; Song, Xinyuan 2; Sun, Liuquan3
刊名	COMPUTATIONAL STATISTICS & DATA ANALYSIS
	2018-09-01
卷号	125 页码:119-135
关键词	Additive hazards models Group penalty Kernel smoothing Local sparsity Oracle property Varying coefficients
ISSN号	0167-9473
DOI	10.1016/j.csda.2018.04.003
英文摘要	Varying coefficient models have numerous applications in a wide scope of scientific areas. Existing methods in varying coefficient models have mainly focused on estimation and variable selection. Besides selecting relevant predictors and estimating their effects, identifying the subregions in which varying coefficients are zero is important to deeply understand the local sparse feature of the functional effects of significant predictors. In this article, we propose a novel method to simultaneously conduct variable selection and identify the local sparsity of significant predictors in the context of varying coefficient additive hazards models. This method combines kernel estimation procedure and the idea of group penalty. The asymptotic properties of the resulting estimators are established. Simulation studies demonstrate that the proposed method can effectively select important predictors and simultaneously identify the null regions of varying coefficients. An application to a nursing home data set is presented. (C) 2018 Elsevier B.V. All rights reserved.
资助项目	Research Grant Council of the Hong Kong Special Administration Region[GRF 14601115] ; Chinese University of Hong Kong ; National Natural Science Foundation of China[11690015] ; National Natural Science Foundation of China[11771431] ; National Natural Science Foundation of China[11471277] ; Key Laboratory of RCSDS, CAS[2008DP173182]
WOS研究方向	Computer Science ; Mathematics
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000433655200009
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/30542]
专题	应用数学研究所
通讯作者	Qu, Lianqiang
作者单位	1.Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China 2.Chinese Univ Hong Kong, Dept Stat, Hong Kong, Hong Kong, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Qu, Lianqiang,Song, Xinyuan,Sun, Liuquan. Identification of local sparsity and variable selection for varying coefficient additive hazards models[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS,2018,125:119-135.
APA	Qu, Lianqiang,Song, Xinyuan,&Sun, Liuquan.(2018).Identification of local sparsity and variable selection for varying coefficient additive hazards models.COMPUTATIONAL STATISTICS & DATA ANALYSIS,125,119-135.
MLA	Qu, Lianqiang,et al."Identification of local sparsity and variable selection for varying coefficient additive hazards models".COMPUTATIONAL STATISTICS & DATA ANALYSIS 125(2018):119-135.