IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v54y2025i3p938-948.html
   My bibliography  Save this article

On the variance estimator and its bounds in general linear models under linear restrictions

Author

Listed:
  • Zaixing Li
  • Changlei Liu
  • Menghan Yi

Abstract

In the article, the error variance estimator σ^2 and its finite sample size bounds are investigated in the framework of a general linear model under linear restrictions (gLMLR) where no distributions are assumed except for some moment conditions and the covariance matrix of the response may be singular, which extends the results in Lindholm and Wahl (2020). In particular, we overcome the difficulty that the matrix of quadratic form in σ^2 is not idempotent. The estimator is obtained by the proposed restricted and proxy-type generalized least square procedure. Meanwhile, the estimable function of the regression coefficient β is estimated, too. Besides, the finite sample properties of these estimators and their consistency are also established. In the end, a simulation study and a real data analysis are done to examine the finite performance of these estimators.

Suggested Citation

  • Zaixing Li & Changlei Liu & Menghan Yi, 2025. "On the variance estimator and its bounds in general linear models under linear restrictions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(3), pages 938-948, February.
    • Handle: RePEc:taf:lstaxx:v:54:y:2025:i:3:p:938-948
    DOI: 10.1080/03610926.2024.2328162
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2024.2328162
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2024.2328162?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:54:y:2025:i:3:p:938-948. 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.