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Quantile regression of longitudinal data with informative observation times
Chen, Xuerong1; Tang, Niansheng4; Zhou, Yong2,3
2016-02-01
Source PublicationJOURNAL OF MULTIVARIATE ANALYSIS
ISSN0047-259X
Volume144Pages:176-188
AbstractLongitudinal data are frequently encountered in medical follow-up studies and economic research. Conditional mean regression and conditional quantile regression are often used to fit longitudinal data. Many methods focused on the cases where the observation times are independent of the response variables or conditionally independent of them given the covariates. Few papers have considered the case where the response variables depend on the observation times or observation times are random variables associated with a counting process. In this paper, we propose a marginally conditional quantile regression approach for modeling longitudinal data with random observing times and informative observation times. Estimators of the parameters in the proposed conditional quantile regression are derived by constructing non-smooth estimating equations when the observation times follow a counting process. Consistency and asymptotic normality for these estimators are established. Asymptotic variance is estimated based on a resampling method. A simulation study is conducted and suggests that the finite sample performance of the proposed approach is very good, and an illustrative approach is provided. (C) 2015 Elsevier Inc. All rights reserved.
KeywordEstimating equation Informative observation times Longitudinal data Quantile regression Resampling method
DOI10.1016/j.jmva.2015.11.007
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[11501461] ; National Natural Science Foundation of China (NSFC)[71271128] ; Fundamental Research Funds for the Central Universities[JBK140507] ; Fundamental Research Funds for the Central Universities[JBK141111] ; National Science Fund for Distinguished Young Scholars of China[11225103] ; State Key Program of National Natural Science Foundation of China[71331006] ; NCMIS ; Key Laboratory of RCSDS, CAS ; Shanghai First-class Discipline A and IRTSHUFE, PCSIRT[IRT13077]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000368467500013
PublisherELSEVIER INC
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/21805
Collection应用数学研究所
Affiliation1.Southwestern Univ Finance & Econ, Sch Stat, Chengdu, Sichuan, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
3.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China
4.Yunnan Univ, Dept Stat, Kunming, Yunnan, Peoples R China
Recommended Citation
GB/T 7714
Chen, Xuerong,Tang, Niansheng,Zhou, Yong. Quantile regression of longitudinal data with informative observation times[J]. JOURNAL OF MULTIVARIATE ANALYSIS,2016,144:176-188.
APA Chen, Xuerong,Tang, Niansheng,&Zhou, Yong.(2016).Quantile regression of longitudinal data with informative observation times.JOURNAL OF MULTIVARIATE ANALYSIS,144,176-188.
MLA Chen, Xuerong,et al."Quantile regression of longitudinal data with informative observation times".JOURNAL OF MULTIVARIATE ANALYSIS 144(2016):176-188.
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