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Identification of local sparsity and variable selection for varying coefficient additive hazards models
Qu, Lianqiang1; Song, Xinyuan2; Sun, Liuquan3
2018-09-01
Source PublicationCOMPUTATIONAL STATISTICS & DATA ANALYSIS
ISSN0167-9473
Volume125Pages:119-135
AbstractVarying 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.
KeywordAdditive hazards models Group penalty Kernel smoothing Local sparsity Oracle property Varying coefficients
DOI10.1016/j.csda.2018.04.003
Language英语
Funding ProjectResearch 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 Research AreaComputer Science ; Mathematics
WOS SubjectComputer Science, Interdisciplinary Applications ; Statistics & Probability
WOS IDWOS:000433655200009
PublisherELSEVIER SCIENCE BV
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/30543
Collection应用数学研究所
Corresponding AuthorQu, Lianqiang
Affiliation1.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
Recommended Citation
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.
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