IDEAS home Printed from https://ideas.repec.org/a/taf/tstfxx/v6y2022i2p89-99.html
   My bibliography  Save this article

A review of distributed statistical inference

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24754269.2021.1974158
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/24754269.2021.1974158?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiang, Pengcheng & Zhou, Ling & Tang, Lu, 2024. "Transfer learning via random forests: A one-shot federated approach," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    2. George Karabatsos, 2024. "Copula Approximate Bayesian Computation Using Distribution Random Forests," Stats, MDPI, vol. 7(3), pages 1-49, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tstfxx:v:6:y:2022:i:2:p:89-99. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tstf .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.