An effective method to reduce the computational complexity of composite quantile regression

doi:10.1007/s00180-017-0749-8

	An effective method to reduce the computational complexity of composite quantile regression
	Wu, Yanke ; Tian, Maozai
	2017-12
发表期刊	COMPUTATIONAL STATISTICS
卷号	32 期号:4 页码:1375-1393
摘要	In this article, we aim to reduce the computational complexity of the recently proposed composite quantile regression (CQR). We propose a new regression method called infinitely composite quantile regression (ICQR) to avoid the determination of the number of uniform quantile positions. Unlike the composite quantile regression, our proposed ICQR method allows combining continuous and infinite quantile positions. We show that the proposed ICQR criterion can be readily transformed into a linear programming problem. Furthermore, the computing time of the ICQR estimate is far less than that of the CQR, though it is slightly larger than that of the quantile regression. The oracle properties of the penalized ICQR are also provided. The simulations are conducted to compare different estimators. A real data analysis is used to illustrate the performance.
关键词	Quantile regression Composite quantile regression Computational complexity Linear programming Dual problem
DOI	10.1007/s00180-017-0749-8
收录类别	SCI ; SCIE
ISSN	0943-4062
语种	英语
WOS研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:000413025300008
出版者	SPRINGER HEIDELBERG
原始文献类型	Article
EISSN	1613-9658
引用统计	被引频次：1[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/11694
专题	统计与数据科学学院
作者单位	1.Guangdong Ocean Univ, Coll Sci, Zhanjiang 524088, Guangdong, Peoples R China; 2.Renmin Univ China, Sch Stat, Ctr Appl Stat, Beijing 100872, Peoples R China; 3.Lanzhou Univ Finance & Econ, Sch Stat, Lanzhou 730101, Gansu, Peoples R China; 4.Xinjiang Univ Finance & Econ, Sch Stat & Informat, Urumqi 830012, Xinjiang, Peoples R China
推荐引用方式 GB/T 7714	Wu, Yanke,Tian, Maozai. An effective method to reduce the computational complexity of composite quantile regression[J]. COMPUTATIONAL STATISTICS,2017,32(4):1375-1393.
APA	Wu, Yanke,&Tian, Maozai.(2017).An effective method to reduce the computational complexity of composite quantile regression.COMPUTATIONAL STATISTICS,32(4),1375-1393.
MLA	Wu, Yanke,et al."An effective method to reduce the computational complexity of composite quantile regression".COMPUTATIONAL STATISTICS 32.4(2017):1375-1393.

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