IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v66y2025i6d10.1007_s00362-025-01744-4.html
   My bibliography  Save this article

A two-sample test for high-dimensional mean vectors via double verification

Author

Listed:
  • Ruizhe Jiang

    (Jinan University)

  • Xiaowen Huang

    (Jinan University)

  • Yunlu Jiang

    (Jinan University)

Abstract

Testing the equality of two high-dimensional mean vectors is a fundamental and important statistical problem. The majority of existing methodological frameworks are limited to either dense or sparse cases. In this paper, we propose a novel framework that incorporates a double validation test statistic designed to be valid for both dense and sparse alternatives by combining the test statistic via the random integration of the difference technique and the extreme-type test statistic. The new framework enables a more tailored and efficient process, contingent on the specific circumstances. Additionally, we propose a data-driven procedure for selecting weight to increase the power of the proposed test. Furthermore, we show the asymptotic properties of the proposed test. Numerical simulations and a real data analysis illustrate the promising performances of our proposed approach.

Suggested Citation

  • Ruizhe Jiang & Xiaowen Huang & Yunlu Jiang, 2025. "A two-sample test for high-dimensional mean vectors via double verification," Statistical Papers, Springer, vol. 66(6), pages 1-24, October.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:6:d:10.1007_s00362-025-01744-4
    DOI: 10.1007/s00362-025-01744-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-025-01744-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00362-025-01744-4?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;

    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:spr:stpapr:v:66:y:2025:i:6:d:10.1007_s00362-025-01744-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.