Asymptotic Approximations to the Bayes Posterior Risk

Toufxk Zoubeidi

Research output: Contribution to journalArticlepeer-review


Suppose that, given [formula ommitted] X1, X2,… and Y1, Y2,… are independent random variables and their respective distribution functions [formula omitted] and [formula omitted] belong to a one parameter exponential family of distributions. We derive approximations to the posterior probabilities of ω lying in closed convex subsets of the parameter space under a general prior density. Using this, we then approximate the Bayes posterior risk for testing the hypotheses [formula omitted] versus [formula omitted] using a zero-one loss function, where Ω1, and Ω2 are disjoint closed convex subsets of the parameter space.

Original languageEnglish
Pages (from-to)99-116
Number of pages18
JournalJournal of Applied Mathematics and Stochastic Analysis
Issue number2
Publication statusPublished - 1990
Externally publishedYes


  • Bayes risk
  • exponential families of distributions
  • indifference zone
  • testing hypotheses

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Applied Mathematics


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