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A review of distributed statistical inference

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  • Yuan Gao
  • Weidong Liu
  • Hansheng Wang
  • Xiaozhou Wang
  • Yibo Yan
  • Riquan Zhang

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 is discussed.

Suggested Citation

  • Yuan Gao & Weidong Liu & Hansheng Wang & Xiaozhou Wang & Yibo Yan & Riquan Zhang, 2022. "A review of distributed statistical inference," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 6(2), pages 89-99, May.
    • Handle: RePEc:taf:tstfxx:v:6:y:2022:i:2:p:89-99
    DOI: 10.1080/24754269.2021.1974158
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