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Estimating moments of the minimum order statistic from normal populations—a bayesian approach

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  • Jean‐François Angers
  • Duncan K. H. Fong

Abstract

Let Yi ∽ N(θi, σ i2), i = 1, …, p, be independently distributed, where θi and σ i2 are unknown. A Bayesian approach is used to estimate the first two moments of the minimum order statistic, W = min (Y1, …, Yp). In order to compute the Bayes estimates, one has to evaluate the predictive densities of the Yi's conditional on past data. Although the required predictive densities are complicated in form, an efficient algorithm to calculate them has been developed and given in the article. An application of the Bayesian method in a continuous‐review control model with multiple suppliers is discussed. © 1994 John Wiley & Sons, Inc.

Suggested Citation

  • Jean‐François Angers & Duncan K. H. Fong, 1994. "Estimating moments of the minimum order statistic from normal populations—a bayesian approach," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(7), pages 1007-1017, December.
    • Handle: RePEc:wly:navres:v:41:y:1994:i:7:p:1007-1017
    DOI: 10.1002/1520-6750(199412)41:73.0.CO;2-#
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    References listed on IDEAS

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    4. Geweke, John, 1988. "Antithetic acceleration of Monte Carlo integration in Bayesian inference," Journal of Econometrics, Elsevier, vol. 38(1-2), pages 73-89.
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