A review of distributed statistical inference

The rapid emergence of massive datasets in various fields poses a serious challenge to traditional statistical methods. Meanwhile, it provides opportunities for researchers to develop novel algorithms. Inspired by the idea of divide-and-conquer, various distributed frameworks for statistical estimat...

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Bibliographic Details
Published inStatistical theory and related fields Vol. 6; no. 2; pp. 89 - 99
Main Authors Gao, Yuan, Liu, Weidong, Wang, Hansheng, Wang, Xiaozhou, Yan, Yibo, Zhang, Riquan
Format Journal Article
LanguageEnglish
Published Taylor & Francis 27.05.2022
Taylor & Francis Group
Subjects
bootstrap
communication-efficiency
Distributed computing
divide-and-conquer
feature screening
nonparametric estimation
principal component analysis
shrinkage methods
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Summary:The rapid emergence of massive datasets in various fields poses a serious challenge to traditional statistical methods. Meanwhile, it provides opportunities for researchers to develop novel algorithms. Inspired by the idea of divide-and-conquer, various distributed frameworks for statistical estimation and inference have been proposed. They were developed to deal with large-scale statistical optimization problems. This paper aims to provide a comprehensive review for related literature. It includes parametric models, nonparametric models, and other frequently used models. Their key ideas and theoretical properties are summarized. The trade-off between communication cost and estimate precision together with other concerns is discussed.
ISSN:2475-4269
2475-4277
DOI:10.1080/24754269.2021.1974158