Advanced Search
MyIDEAS: Login

A Unified Approach to Estimating a Normal Mean Matrix in High and Low Dimensions

Contents:

Author Info

  • Hisayuki Tsukuma

    (Faculty of Medicine, Toho University)

  • Tatsuya Kubokawa

    (Faculty of Economics, The University of Tokyo)

Registered author(s):

    Abstract

       This paper addresses the problem of estimating the normal mean matrix with an unknown covariance matrix. Motivated by an empirical Bayes method, we suggest a uni ed form of the Efron-Morris type estimators based on the Moore-Penrose inverse. This form not only can be de ned for any dimension and any sample size, but also can contain the Efron-Morris type or Baranchik type estimators suggested so far in the literature. Also, the uni ed form suggests a general class of shrinkage estimators. For shrinkage estimators within the general class, a uni ed expression of unbiased estimators of the risk functions is derived regardless of the dimension of covariance matrix and the size of the mean matrix. An analytical dominance result is provided for a positive-part rule of the shrinkage estimators.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2014/2014cf926.pdf
    Download Restriction: no

    Bibliographic Info

    Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-926.

    as in new window
    Length: 23 pages
    Date of creation: Mar 2014
    Date of revision:
    Handle: RePEc:tky:fseres:2014cf926

    Contact details of provider:
    Postal: Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033
    Phone: +81-3-5841-5644
    Fax: +81-3-5841-8294
    Email:
    Web page: http://www.cirje.e.u-tokyo.ac.jp/index.html
    More information through EDIRC

    Related research

    Keywords:

    This paper has been announced in the following NEP Reports:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:tky:fseres:2014cf926. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CIRJE administrative office).

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.