IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i6p1605-1621.html
   My bibliography  Save this article

The weighted ridge estimation for linear mixed models with measurement error under stochastic linear mixed restrictions

Author

Listed:
  • Bahareh Yavarizadeh
  • S. Ejaz Ahmed

Abstract

In this paper, we introduce a new ridge-type estimator/predictor called the weighted mixed ridge estimator/predictor (WMRE/P) by unifying the sample and prior information in a linear mixed model with measurement error on fixed effects, when additional stochastic linear restrictions are assumed to have fixed and random effects. The asymptotic normality property of the proposed estimator will be derived, and the necessary and sufficient conditions for the superiority of the WMRE/P over the weighted mixed estimator/predictor (WME/P) and mixed ridge estimator/predictor (MRE/P) will be obtained in terms of mean square error matrix (MSEM) criterion. Finally, the theoretical findings of the proposed estimator are illustrated using a data example and a Monte Carlo simulation.

Suggested Citation

  • Bahareh Yavarizadeh & S. Ejaz Ahmed, 2022. "The weighted ridge estimation for linear mixed models with measurement error under stochastic linear mixed restrictions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(6), pages 1605-1621, March.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:6:p:1605-1621
    DOI: 10.1080/03610926.2021.1927097
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1927097
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.1927097?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:51:y:2022:i:6:p:1605-1621. 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.