IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v45y2016i12p3675-3691.html
   My bibliography  Save this article

Empirical Bayes estimation for the mean of the selected normal population when there are additional data

Author

Listed:
  • Zohreh Mohammadi
  • Mina Towhidi

Abstract

This paper is concerned with the problem of estimation for the mean of the selected population from two normal populations with unknown means and common known variance in a Bayesian framework. The empirical Bayes estimator, when there are available additional observations, is derived and its bias and risk function are computed. The expected bias and risk of the empirical Bayes estimator and the intuitive estimator are compared. It is shown that the empirical Bayes estimator is asymptotically optimal and especially dominates the intuitive estimator in terms of Bayes risk, with respect to any normal prior. Also, the Bayesian correlation between the mean of the selected population (random parameter) and some interested estimators are obtained and compared.

Suggested Citation

  • Zohreh Mohammadi & Mina Towhidi, 2016. "Empirical Bayes estimation for the mean of the selected normal population when there are additional data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(12), pages 3675-3691, June.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:12:p:3675-3691
    DOI: 10.1080/03610926.2014.904359
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2014.904359
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2014.904359?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:45:y:2016:i:12:p:3675-3691. 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.