Statistical inference in massive datasets by empirical likelihood

被引：2

作者：

Ma, Xuejun ^{[1
]}

Wang, Shaochen ^{[2
]}

Zhou, Wang ^{[3
]}

机构：

[1] Soochow Univ, Sch Math Sci, Suzhou 215006, Peoples R China

[2] South China Univ Technol, Sch Math, Guangzhou 510640, Peoples R China

[3] Natl Univ Singapore, Dept Stat & Data Sci, Singapore 117546, Singapore

来源：

COMPUTATIONAL STATISTICS | 2022年 / 37卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Bootstrap; Divide-and-conquer; Hypothesis test; Empirical likelihood; REGRESSION-ANALYSIS; REPRESENTATION;

D O I：

10.1007/s00180-021-01153-9

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper, we propose a new statistical inference method for massive data sets, which is very simple and efficient by combining divide-and-conquer method and empirical likelihood. Compared with two popular methods (the bag of little bootstrap and the subsampled double bootstrap), we make full use of data sets, and reduce the computation burden. Extensive numerical studies and real data analysis demonstrate the effectiveness and flexibility of our proposed method. Furthermore, the asymptotic property of our method is derived.

引用

页码：1143 / 1164

页数：22

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