IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/22416.html
   My bibliography  Save this paper

Robustness of Bayes decisions for normal and lognormal distributions under hierarchical priors

Author

Listed:
  • Sinha, Pankaj
  • Jayaraman, Prabha

Abstract

In this paper we derive the Bayes estimates of the location parameter of normal and lognormal distribution under the hierarchical priors for the vector parameter, . The ML-II ε-contaminated class of priors are employed at the second stage of hierarchical priors to examine the robustness of Bayes estimates with respect to possible misspecification at the second stage. The simulation studies for both normal and lognormal distributions confirm Berger’s (1985) assertion that form of the second stage prior does not affect the Bayes decisions.

Suggested Citation

  • Sinha, Pankaj & Jayaraman, Prabha, 2010. "Robustness of Bayes decisions for normal and lognormal distributions under hierarchical priors," MPRA Paper 22416, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:22416
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/22416/1/MPRA_paper_22416.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. James Berger & Elías Moreno & Luis Pericchi & M. Bayarri & José Bernardo & Juan Cano & Julián Horra & Jacinto Martín & David Ríos-Insúa & Bruno Betrò & A. Dasgupta & Paul Gustafson & Larry Wasserman &, 1994. "An overview of robust Bayesian analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 3(1), pages 5-124, June.
    2. Berger, J. & Berliner, L.M., 1984. "Bayesian input in Stein estimation and a new minimax empirical Bayes estimator," Journal of Econometrics, Elsevier, vol. 25(1-2), pages 87-108.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2018. "Robust linear static panel data models using ε-contamination," Journal of Econometrics, Elsevier, vol. 202(1), pages 108-123.
    2. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2014. "Robust linear static panel data models using epsilon-contamination," MPRA Paper 59896, University Library of Munich, Germany.
    3. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2014. "Robust Linear Static Panel Data Models Using ?-Contamination," IZA Discussion Papers 8661, Institute for the Study of Labor (IZA).

    More about this item

    Keywords

    Hierarchical Bayes; Hierarchical priors; ML-II ε-contaminated class of priors;

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:22416. 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: (Joachim Winter) or (Rebekah McClure). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    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 CitEc recognized a reference but did not link an item in RePEc 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.