KMS Of Academy of mathematics and systems sciences, CAS
| Quantile regression in big data: A divide and conquer based strategy | |
Chen, Lanjue1,3,4; Zhou, Yong1,2
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| 2020-04-01 | |
| Source Publication | COMPUTATIONAL STATISTICS & DATA ANALYSIS
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| ISSN | 0167-9473 |
| Volume | 144Pages:17 |
| Abstract | 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. |
| Keyword | Data stream Divide and conquer Estimating equation Massive data sets Quantile regression |
| DOI | 10.1016/j.csda.2019.106892 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 Research Area | Computer Science ; Mathematics |
| WOS Subject | Computer Science, Interdisciplinary Applications ; Statistics & Probability |
| WOS ID | WOS:000515446200029 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/50841 |
| Collection | 应用数学研究所 |
| Corresponding Author | Zhou, Yong |
| Affiliation | 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 |
| Recommended Citation 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. |
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