Efficient Quantile Regression Analysis With Missing Observations

doi:10.1080/01621459.2014.928219

CSpace > 应用数学研究所

	Efficient Quantile Regression Analysis With Missing Observations
	Chen, Xuerong 1; Wan, Alan T. K.2; Zhou, Yong3,4
	2015-06-01
发表期刊	JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN	0162-1459
卷号	110 期号:510 页码:723-741
摘要	This article examines the problem of estimation in a quantile regression model when observations are missing at random under independent and nonidentically distributed errors. We consider three approaches of handling this problem based on nonparametric inverse probability weighting, estimating equations projection, and a combination of both. An important distinguishing feature of our methods is their ability to handle missing response and/or partially missing covariates, whereas existing techniques can handle only one or the other, but not both. We prove that our methods yield asymptotically equivalent estimators that achieve the desirable asymptotic properties of unbiasedness, normality, and root n-consistency. Because we do not assume that the errors are identically distributed, our theoretical results are valid under heteroscedasticity, a particularly strong feature of our methods. Under the special case of identical error distributions, all of our proposed estimators achieve the semiparametric efficiency bound. To facilitate the practical implementation of these methods, we develop an iterative method based on the majorize/minimize algorithm for computing the quantile regression estimates, and a bootstrap method for computing their variances. Our simulation findings suggest that all three methods have good finite sample properties. We further illustrate these methods by a real data example. Supplementary materials for this article are available online.
关键词	Estimating equations Missing at random Resampling method Semiparametric efficient
DOI	10.1080/01621459.2014.928219
语种	英语
资助项目	City University of Hong Kong ; Hong Kong Research Grants Council[CityU - 11302914] ; National Natural Science Foundation of China (NSFC)[71271128] ; National Natural Science Foundation of China[71331006] ; NCMIS ; Shanghai Leading Academic Discipline Project A ; IRTSHUFE
WOS研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:000357437300021
出版者	AMER STATISTICAL ASSOC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/20262
专题	应用数学研究所
通讯作者	Chen, Xuerong
作者单位	1.Georgetown Univ, Biostat & Bioinformat, Washington, DC 20057 USA 2.City Univ Hong Kong, Dept Management Sci, Hong Kong, Hong Kong, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 4.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Chen, Xuerong,Wan, Alan T. K.,Zhou, Yong. Efficient Quantile Regression Analysis With Missing Observations[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,2015,110(510):723-741.
APA	Chen, Xuerong,Wan, Alan T. K.,&Zhou, Yong.(2015).Efficient Quantile Regression Analysis With Missing Observations.JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,110(510),723-741.
MLA	Chen, Xuerong,et al."Efficient Quantile Regression Analysis With Missing Observations".JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 110.510(2015):723-741.