IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-56773-6_20.html
   My bibliography  Save this book chapter

Estimation of the Common Mean of Two Multivariate Normal Distributions Under Symmetrical and Asymmetrical Loss Functions

In: Recent Developments in Multivariate and Random Matrix Analysis

Author

Listed:
  • Dan Zhuang

    (Fujian Normal University)

  • S. Ejaz Ahmed

    (Brock University)

  • Shuangzhe Liu

    (University of Canberra)

  • Tiefeng Ma

    (Southwestern University of Finance and Economics)

Abstract

In this paper, the estimation of the common mean vector of two multivariate normal populations is considered and a new class of unbiased estimators is proposed. Several dominance results under the quadratic loss and LINEX loss functions are established. To illustrate the usefulness of these estimators, a simulation study with finite samples is conducted to compare them with four existing estimators, including the sample mean and the Graybill-Deal estimator. Based on the comparison studies, we found that the numerical performance of the proposed estimators is almost as good as μ ~ C C $$\tilde {\mu }_{CC}$$ proposed by Chiou and Cohen (Ann Inst Stat Math 37:499–506, 1985) in terms of the risks. Its theoretical dominance over the sample mean of a single population under the sufficient conditions given is also established.

Suggested Citation

  • Dan Zhuang & S. Ejaz Ahmed & Shuangzhe Liu & Tiefeng Ma, 2020. "Estimation of the Common Mean of Two Multivariate Normal Distributions Under Symmetrical and Asymmetrical Loss Functions," Springer Books, in: Thomas Holgersson & Martin Singull (ed.), Recent Developments in Multivariate and Random Matrix Analysis, chapter 0, pages 351-373, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-56773-6_20
    DOI: 10.1007/978-3-030-56773-6_20
    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:978-3-030-56773-6_20. 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.