IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v52y2023i18p6475-6486.html
   My bibliography  Save this article

Using an inequality constraint to increase the power of the homogeneity tests for a two-sample problem with a mixture structure

Author

Listed:
  • Guanfu Liu
  • Rongji Mu
  • Yang Liu
  • Zhimei Sheng

Abstract

In recent years, two-sample testing problems with one sample from a mixture distribution have been studied in the literature. Some of these studies ignore an auxiliary information that the exposure or treatment involved in the two-sample problems may have a positive (or negative) effect on the response variable if the effect exists. The positive (or negative) effect implies an inequality constraint. In this paper, we establish the homogeneity tests for the two-sample problem with a mixture structure by considering the inequality constraint, and their null limiting distributions are shown to be a mixture of chi-square distributions. Simulation studies and real data analysis show that the proposed tests have better performance than the existing methods.

Suggested Citation

  • Guanfu Liu & Rongji Mu & Yang Liu & Zhimei Sheng, 2023. "Using an inequality constraint to increase the power of the homogeneity tests for a two-sample problem with a mixture structure," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(18), pages 6475-6486, September.
  • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:18:p:6475-6486
    DOI: 10.1080/03610926.2022.2031216
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2022.2031216
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03610926.2022.2031216?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:52:y:2023:i:18:p:6475-6486. 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.