IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v34y2025i4d10.1007_s10260-025-00804-1.html
   My bibliography  Save this article

Some permutation tests for high dimensional mean vectors

Author

Listed:
  • Caizhu Huang

    (Guandong University of Finance and Economics
    University of Padova)

  • Euloge Clovis Kenne Pagui

    (University of Oslo)

  • Fortunato Pesarin

    (University of Padova)

Abstract

For high dimensional data where the dimension p of the observation vector is larger than the group sample sizes $$n_i, i=1,2$$ n i , i = 1 , 2 , classical approaches based on asymptotic theory are no longer valid. A recent parametric projection test shows a comparable type I error but with a substantial power improvement. However, the asymptotic theory of all such statistics may not hold with small $$n_i$$ n i , especially, in the presence of strong correlation structures. We here propose the use of a permutation test based on the same parametric projection test statistic. Extensive simulation results show that the permutation versions are more accurate under the null hypothesis than the parametric projection test when sample sizes are small and, especially when there is a strong correlation in the data. A significant test of “Sell in May and Go Away” for the manufacturing sector in China’s A-share market from May 2001 to October 2017 illustrates its application.

Suggested Citation

  • Caizhu Huang & Euloge Clovis Kenne Pagui & Fortunato Pesarin, 2025. "Some permutation tests for high dimensional mean vectors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 34(4), pages 753-765, September.
    • Handle: RePEc:spr:stmapp:v:34:y:2025:i:4:d:10.1007_s10260-025-00804-1
    DOI: 10.1007/s10260-025-00804-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-025-00804-1
    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/s10260-025-00804-1?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:stmapp:v:34:y:2025:i:4:d:10.1007_s10260-025-00804-1. 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.