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

doi:10.1016/j.csda.2018.04.003

CSpace > 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
	2018-09-01
发表期刊	COMPUTATIONAL STATISTICS & DATA ANALYSIS
ISSN	0167-9473
卷号	125 页码:119-135
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.
关键词	Additive hazards models Group penalty Kernel smoothing Local sparsity Oracle property Varying coefficients
DOI	10.1016/j.csda.2018.04.003
语种	英语
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
WOS类目	Computer Science, Interdisciplinary Applications ; Statistics & Probability
WOS记录号	WOS:000433655200009
出版者	ELSEVIER SCIENCE BV
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/30543
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.