IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v50y2021i22p5399-5410.html
   My bibliography  Save this article

Generalized FWER control procedures for testing multiple hypotheses

Author

Listed:
  • Haibing Zhao

Abstract

Most methods for testing multiple hypotheses assume the monotone likelihood ratio (MLR) condition holds. In fact, in some cases, MLR does not hold, and the widely used Bonferroni procedure may be inefficient. In this paper, we aim to improve the Bonferroni procedure without assuming the MLR condition holds. By combining the advantages of the test method constructed according to the Neyman–Pearson lemma and the Bonferroni procedure, we propose two generalized Bonferroni procedures, denoted as GB1 and GB2. Further, we propose to plug the proportion of true null hypotheses in the GB2 procedure to improve power. We numerically compare the proposed methods with existing methods. Simulation results show that the proposed methods perform very close to or significantly better than existing ones; the proposed plug-in procedure performs best among all in terms of power performance. Two real data sets are analyzed with the proposed procedures.

Suggested Citation

  • Haibing Zhao, 2021. "Generalized FWER control procedures for testing multiple hypotheses," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(22), pages 5399-5410, November.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:22:p:5399-5410
    DOI: 10.1080/03610926.2020.1728555
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2020.1728555
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2020.1728555?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.

    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:lstaxx:v:50:y:2021:i:22:p:5399-5410. 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/lsta .

    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.