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Bayesian optimization analysis with ML-II ε-contaminated prior

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Listed:
  • Pankaj Sinha
  • Ashok Bansal

Abstract

In this paper we derive the predictive density function of a future observation when prior distribution for unknown mean of a normal population is a Type-II maximum likelihood ε-contaminated prior. The derived predictive distribution is applied to the problem of optimization of a regression nature in the decisive prediction framework.

Suggested Citation

  • Pankaj Sinha & Ashok Bansal, 2008. "Bayesian optimization analysis with ML-II ε-contaminated prior," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(2), pages 203-211.
    • Handle: RePEc:taf:japsta:v:35:y:2008:i:2:p:203-211
    DOI: 10.1080/02664760701775415
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    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.
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    Cited by:

    1. Sinha, Pankaj & Jayaraman, Prabha, 2009. "Bayes reliability measures of Lognormal and inverse Gaussian distributions under ML-II ε-contaminated class of prior distributions," MPRA Paper 16528, University Library of Munich, Germany.
    2. Sinha, Pankaj & Jayaraman, Prabha, 2009. "Robustness of Bayesian results for Inverse Gaussian distribution under ML-II epsilon-contaminated and Edgeworth Series class of prior distributions," MPRA Paper 15396, University Library of Munich, Germany.

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