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arXiv · 2304.06245

A review of distributed statistical inference

Yuan Gao·Weidong Liu·Hansheng Wang·Xiaozhou Wang·Yibo Yan·Riquan Zhang
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Abstract

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 are discussed.

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Yuan Gao, Weidong Liu, Hansheng Wang, Xiaozhou Wang, Yibo Yan, Riquan Zhang. 2023-04-13. A review of distributed statistical inference. https://doi.org/10.1080/24754269.2021.1974158

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