IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v52y2023i16p5799-5813.html
   My bibliography  Save this article

Estimation of the slope parameter in a linear regression model under a bounded loss function

Author

Listed:
  • Z. Mahdizadeh
  • M. Naghizadeh Qomi
  • Shahjahan Khan

Abstract

The estimation of the slope parameter of a simple linear regression model in the presence of nonsample prior information under the reflected normal loss function is considered. Usually, the traditional estimation methods such as the least squared (LS) error are used to estimate the slope parameter. Sometimes the researcher has information about the unknown slope parameter from experience as a point guess, the nonsample prior information. In this paper, the shrinkage pretest estimators are introduced and their risk functions are derived under the reflected normal loss function. Several methods of finding distrust coefficient of the shrinkage pretest estimators are proposed. The behavior of the estimators are compared using a simulation study. The results show that the shrinkage pretest estimator outperforms the LS estimator when nonsample prior information is close to the real value. A real data set is analyzed for illustrating the results.

Suggested Citation

  • Z. Mahdizadeh & M. Naghizadeh Qomi & Shahjahan Khan, 2023. "Estimation of the slope parameter in a linear regression model under a bounded loss function," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(16), pages 5799-5813, August.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:16:p:5799-5813
    DOI: 10.1080/03610926.2021.2020292
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.2020292
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.2020292?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:16:p:5799-5813. 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.