Quantile regression methods with varying-coefficient models for censored data

doi:10.1016/j.csda.2015.02.011

CORC > 上海财经大学 > 上海财经大学

	Quantile regression methods with varying-coefficient models for censored data
	Xie, Shangyu 1,2; Wan, Alan T. K.3; Zhou, Yong 4,5
刊名	COMPUTATIONAL STATISTICS & DATA ANALYSIS
	2015-08
卷号	88 页码:154-172
关键词	Composite quantile regression Covariate-dependent censoring Inverse probability weighting MM algorithm Perturbation resampling
ISSN号	0167-9473
DOI	10.1016/j.csda.2015.02.011
英文摘要	Considerable intellectual progress has been made to the development of various semi-parametric varying-coefficient models over the past ten to fifteen years. An important advantage of these models is that they avoid much of the curse of dimensionality problem as the nonparametric functions are restricted only to some variables. More recently, varying-coefficient methods have been applied to quantile regression modeling, but all previous studies assume that the data are fully observed. The main purpose of this paper is to develop a varying-coefficient approach to the estimation of regression quantiles under random data censoring. We use a weighted inverse probability approach to account for censoring, and propose a majorize minimize type algorithm to optimize the non-smooth objective function. The asymptotic properties of the proposed estimator of the nonparametric functions are studied, and a resampling method is developed for obtaining the estimator of the sampling variance. An important aspect of our method is that it allows the censoring time to depend on the covariates. Additionally, we show that this varying-coefficient procedure can be further improved when implemented within a composite quantile regression framework. Composite quantile regression has recently gained considerable attention due to its ability to combine information across different quantile functions. We assess the finite sample properties of the proposed procedures in simulated studies. A real data application is also considered. (C) 2015 Elsevier B.V. All rights reserved.
WOS研究方向	Computer Science ; Mathematics
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000353734300012
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/1513]
专题	上海财经大学
通讯作者	Xie, Shangyu
作者单位	1.Univ Int Business & Econ, RCAF, Beijing, Peoples R China; 2.Univ Int Business & Econ, Sch Banking & Finance, Beijing, Peoples R China; 3.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China; 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China; 5.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Xie, Shangyu,Wan, Alan T. K.,Zhou, Yong. Quantile regression methods with varying-coefficient models for censored data[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS,2015,88:154-172.
APA	Xie, Shangyu,Wan, Alan T. K.,&Zhou, Yong.(2015).Quantile regression methods with varying-coefficient models for censored data.COMPUTATIONAL STATISTICS & DATA ANALYSIS,88,154-172.
MLA	Xie, Shangyu,et al."Quantile regression methods with varying-coefficient models for censored data".COMPUTATIONAL STATISTICS & DATA ANALYSIS 88(2015):154-172.