IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-69111-9_14.html
   My bibliography  Save this book chapter

Shrinkage Estimation of the Intercept Parameter in Linear Regression

In: Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science

Author

Listed:
  • Nahla Elbassouni

    (School of Business and Economics, Linnaeus University, Department of Economics and Statistics)

  • Thomas Holgersson

    (Faculty of Technology, Linnaeus University, Department of Mathematics)

  • Stepan Mazur

    (School of Business, Örebro University, Unit of Statistics
    School of Business and Economics, Linnaeus University, Department of Economics and Statistics)

Abstract

It is well known that the slope parameters in the linear regression model may be subject to high sampling variance when the regressors are non-orthogonal. A vast number of ridge and shrinkage estimators have been proposed to yield improvements over ordinary least squares or maximum likelihood estimators. The intercept parameter, however, has been given very little attention in the context. We propose a number of intercept estimators for models with non-orthogonal regressors that are based on shrinkage techniques. The optimal values of shrinkage coefficients are obtained according to the minimum mean square error criterion. A good performance of proposed estimators is documented.

Suggested Citation

  • Nahla Elbassouni & Thomas Holgersson & Stepan Mazur, 2024. "Shrinkage Estimation of the Intercept Parameter in Linear Regression," Springer Books, in: Sven Knoth & Yarema Okhrin & Philipp Otto (ed.), Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science, pages 279-293, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-69111-9_14
    DOI: 10.1007/978-3-031-69111-9_14
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below 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
    for a similarly titled item that would be available.

    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:spr:sprchp:978-3-031-69111-9_14. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.