IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

Nonparametric inference for stochastic linear hypotheses: Application to high-dimensional data

Listed author(s):
  • Jeanne Kowalski
Registered author(s):

    The Mann--Whitney--Wilcoxon rank sum test is limited to comparison of two groups with univariate responses. In this paper, we introduce a class of stochastic linear hypotheses that addresses these limitations within a nonparametric setting. We formulate hypotheses for simultaneous comparisons of several, multivariate response groups, without modelling the response distributions. Inference is developed based on U-statistics theory and an exchangeability assumption. The latter condition is required to identify testable hypotheses for high-dimensional response vectors, such as those arising in genomic and psychosocial research. The methodology is illustrated with two real-data applications. Copyright Biometrika Trust 2004, Oxford University Press.

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below under "Related research" whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Article provided by Biometrika Trust in its journal Biometrika.

    Volume (Year): 91 (2004)
    Issue (Month): 2 (June)
    Pages: 393-408

    in new window

    Handle: RePEc:oup:biomet:v:91:y:2004:i:2:p:393-408
    Contact details of provider: Postal:
    Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK

    Fax: 01865 267 985
    Web page:

    Order Information: Web:

    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:oup:biomet:v:91:y:2004:i:2:p:393-408. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Oxford University Press)

    or (Christopher F. Baum)

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.