CSpace
A quasi-residuals method in sliced inverse regression
Tian, MZ; Li, GY
2004-01-15
发表期刊STATISTICS & PROBABILITY LETTERS
ISSN0167-7152
卷号66期号:2页码:205-212
摘要An effective data analytic tool, sliced inverse regression(SIR), for the analysis of multivariate data was developed by Li (Technic Report, Department of Mathematics, UCLA, 1989) and Duan and Li (J. Amer. Statist. Assoc. 86 (1991) 316). It is a method for dimension reduction. Let (Y,X) be a (p + 1)-dimensional random vector, with Y is an element of R-1 and X is an element of R-P. Let Lambda = E{Cov(X/Y)}. Since it is necessary for an estimate of Lambda in the implementation of SIR, Li (1991) considered two-sliced estimate, Hsing and Carroll (Ann. Statist. Assoc. 20 (11992) 1042) derived the asymptotic properties for the estimate and Zhu and Ng (Statist. Sinica. 5 (1995) 727) discussed fixed number-sliced estimate. In this paper, quasi-residuals-based estimate for Lambda is proposed and asymptotic properties obtained. Sample properties are investigated in a simulation study. (C) 2003 Elsevier B.V. All rights reserved.
关键词quasi-residuals sliced inverse regression order statistics concomitant
DOI10.1016/j.spl.2003.09.007
语种英语
WOS研究方向Mathematics
WOS类目Statistics & Probability
WOS记录号WOS:000188229800013
出版者ELSEVIER SCIENCE BV
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/19670
专题中国科学院数学与系统科学研究院
通讯作者Tian, MZ
作者单位Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
推荐引用方式
GB/T 7714
Tian, MZ,Li, GY. A quasi-residuals method in sliced inverse regression[J]. STATISTICS & PROBABILITY LETTERS,2004,66(2):205-212.
APA Tian, MZ,&Li, GY.(2004).A quasi-residuals method in sliced inverse regression.STATISTICS & PROBABILITY LETTERS,66(2),205-212.
MLA Tian, MZ,et al."A quasi-residuals method in sliced inverse regression".STATISTICS & PROBABILITY LETTERS 66.2(2004):205-212.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Tian, MZ]的文章
[Li, GY]的文章
百度学术
百度学术中相似的文章
[Tian, MZ]的文章
[Li, GY]的文章
必应学术
必应学术中相似的文章
[Tian, MZ]的文章
[Li, GY]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。