Quantile regression in big data: A divide and conquer based strategy

doi:10.1016/j.csda.2019.106892

CSpace > 应用数学研究所

	Quantile regression in big data: A divide and conquer based strategy
	Chen, Lanjue 1,3,4; Zhou, Yong1,2
	2020-04-01
发表期刊	COMPUTATIONAL STATISTICS & DATA ANALYSIS
ISSN	0167-9473
卷号	144 页码:17
摘要	Quantile regression, which analyzes the conditional distribution of outcomes given a set of covariates, has been widely used in many fields. However, the volume and velocity of big data make the estimation of quantile regression model extremely difficult due to the intensive computation and the limited storage. Based on divide and conquer strategy, a simple and efficient method is proposed to address this problem. The proposed approach only keeps summary statistics of each data block and then can use them to reconstruct the estimator of the entire data with asymptotically negligible approximation error. This property makes the proposed method particularly appealing when data blocks are retained in multiple servers or come in the form of data stream. Furthermore, the proposed estimator is shown to be consistent and asymptotically as efficient as the estimating equation estimator calculated using the entire data together when certain conditions hold. The merits of the proposed method are illustrated using both simulation studies and real data analysis. (C) 2019 Elsevier B.V. All rights reserved.
关键词	Data stream Divide and conquer Estimating equation Massive data sets Quantile regression
DOI	10.1016/j.csda.2019.106892
收录类别	SCI
语种	英语
资助项目	State Key Program in the Major Research Plan of National Natural Science Foundation of China[91546202] ; State Key Program of National Natural Science Foundation of China[71931004]
WOS研究方向	Computer Science ; Mathematics
WOS类目	Computer Science, Interdisciplinary Applications ; Statistics & Probability
WOS记录号	WOS:000515446200029
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/50841
专题	应用数学研究所
通讯作者	Zhou, Yong
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.East China Normal Univ, Key Lab Adv Theory & Applicat Stat & Data Sci, MOE, Acad Stat & Interdisciplinary Sci, Shanghai 200062, Peoples R China 3.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Chen, Lanjue,Zhou, Yong. Quantile regression in big data: A divide and conquer based strategy[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS,2020,144:17.
APA	Chen, Lanjue,&Zhou, Yong.(2020).Quantile regression in big data: A divide and conquer based strategy.COMPUTATIONAL STATISTICS & DATA ANALYSIS,144,17.
MLA	Chen, Lanjue,et al."Quantile regression in big data: A divide and conquer based strategy".COMPUTATIONAL STATISTICS & DATA ANALYSIS 144(2020):17.