General composite quantile regression: Theory and methods

doi:10.1080/03610926.2019.1568493

	General composite quantile regression: Theory and methods
	Wu, Yanke ; Tian, Maozai; Tang, Man-Lai
	2020-05-02
发表期刊	COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
卷号	49 期号:9 页码:2217-2236
摘要	In this article, we propose a new regression method called general composite quantile regression (GCQR) which releases the unrealistic finite error variance assumption being imposed by the traditional least squares (LS) method. Unlike the recently proposed composite quantile regression (CQR) method, our proposed GCQR allows any continuous non-uniform density/weight function. As a result, determination of the number of uniform quantile positions is not required. Most importantly, the proposed GCQR criterion can be readily transformed to a linear programing problem, which substantially reduces the computing time. Our theoretical and empirical results show that the GCQR is generally efficient than the CQR and LS if the weight function is appropriately chosen. The oracle properties of the penalized GCQR are also provided. Our simulation results are consistent with the derived theoretical findings. A real data example is analyzed to demonstrate our methodologies.
关键词	Asymptotic relative efficiency general composite quantile regression oracle property weight function
DOI	10.1080/03610926.2019.1568493
收录类别	SCI ; EI ; SCOPUS ; SCIE
ISSN	0361-0926
语种	英语
WOS研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:000519155600011
出版者	TAYLOR & FRANCIS INC
EI入藏号	20201208318144
原始文献类型	Article
EISSN	1532-415X
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/424
专题	统计与数据科学学院
作者单位	1.Guangdong Ocean Univ, Sch Math & Comp Sci, Zhanjiang, Guangdong, Peoples R China; 2.Renmin Univ China, Sch Stat, Ctr Appl Stat, Beijing, Peoples R China; 3.Xinjiang Univ Finance & Econ, Sch Stat & Informat, Xinjiang, Peoples R China; 4.Lanzhou Univ Finance & Econ, Sch Stat, Lanzhou, Gansu, Peoples R China; 5.Hang Seng Univ Hong Kong, Dept Math & Stat, Sha Tin, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Wu, Yanke,Tian, Maozai,Tang, Man-Lai. General composite quantile regression: Theory and methods[J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS,2020,49(9):2217-2236.
APA	Wu, Yanke,Tian, Maozai,&Tang, Man-Lai.(2020).General composite quantile regression: Theory and methods.COMMUNICATIONS IN STATISTICS-THEORY AND METHODS,49(9),2217-2236.
MLA	Wu, Yanke,et al."General composite quantile regression: Theory and methods".COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 49.9(2020):2217-2236.