IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v101y2014i4p964-970..html
   My bibliography  Save this article

Analytical p-value calculation for the higher criticism test in finite-d problems

Author

Listed:
  • Ian J. Barnett
  • Xihong Lin

Abstract

The higher criticism test is effective for testing a joint null hypothesis against a sparse alternative, e.g., for testing the effect of a gene or genetic pathway that consists of d genetic markers. Accurate p-value calculations for the higher criticism test based on the asymptotic distribution require a very large d, which is not the case for the number of genetic variants in a gene or a pathway. In this paper we propose an analytical method for accurately computing the p-value of the higher criticism test for finite-d problems. Unlike previous treatments, this method does not rely on asymptotics in d or on simulation, and is exact for arbitrary d when the test statistics are normally distributed. The method is particularly computationally advantageous when d is not large. We illustrate the proposed method with a case-control genome-wide association study of lung cancer and compare its power with competing methods through simulations.

Suggested Citation

  • Ian J. Barnett & Xihong Lin, 2014. "Analytical p-value calculation for the higher criticism test in finite-d problems," Biometrika, Biometrika Trust, vol. 101(4), pages 964-970.
    • Handle: RePEc:oup:biomet:v:101:y:2014:i:4:p:964-970.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asu033
    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.

    Citations

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


    Cited by:

    1. Zhao, Sihai Dave & Cai, T. Tony & Li, Hongzhe, 2017. "Optimal detection of weak positive latent dependence between two sequences of multiple tests," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 169-184.
    2. Anders Bredahl Kock & David Preinerstorfer, 2021. "Superconsistency of Tests in High Dimensions," Papers 2106.03700, arXiv.org, revised Jan 2022.
    3. Ian Barnett & Rajarshi Mukherjee & Xihong Lin, 2017. "The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 64-76, January.

    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:oup:biomet:v:101:y:2014:i:4:p:964-970.. 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.