IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v111y2024i1p93-108..html
   My bibliography  Save this article

Online inference with debiased stochastic gradient descent

Author

Listed:
  • Ruijian Han
  • Lan Luo
  • Yuanyuan Lin
  • Jian Huang

Abstract

SummaryWe propose a debiased stochastic gradient descent algorithm for online statistical inference with high-dimensional data. Our approach combines the debiasing technique developed in high-dimensional statistics with the stochastic gradient descent algorithm. It can be used to construct confidence intervals efficiently in an online fashion. Our proposed algorithm has several appealing aspects: as a one-pass algorithm, it reduces the time complexity; in addition, each update step requires only the current data together with the previous estimate, which reduces the space complexity. We establish the asymptotic normality of the proposed estimator under mild conditions on the sparsity level of the parameter and the data distribution. Numerical experiments demonstrate that the proposed debiased stochastic gradient descent algorithm attains nominal coverage probability. Furthermore, we illustrate our method with analysis of a high-dimensional text dataset.

Suggested Citation

  • Ruijian Han & Lan Luo & Yuanyuan Lin & Jian Huang, 2024. "Online inference with debiased stochastic gradient descent," Biometrika, Biometrika Trust, vol. 111(1), pages 93-108.
    • Handle: RePEc:oup:biomet:v:111:y:2024:i:1:p:93-108.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asad046
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:oup:biomet:v:111:y:2024:i:1:p:93-108.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.