Efficient Quantile Regression Analysis With Missing Observations
文献类型：期刊论文
| 作者 | Chen, Xuerong1; Wan, Alan T. K.2; Zhou, Yong3,4
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| 刊名 | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
 |
| 出版日期 | 2015-06-01 |
| 卷号 | 110期号:510页码:723-741 |
| 关键词 | Estimating equations Missing at random Resampling method Semiparametric efficient |
| ISSN号 | 0162-1459 |
| DOI | 10.1080/01621459.2014.928219 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000357437300021 |
| 出版者 | AMER STATISTICAL ASSOC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/20261]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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