Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Nonparametric Bayesian Multiple Comparisons for Geometric Populations

  • Ali, M. Masoom (Department of Mathematical Sciences, Ball State University) ;
  • Cho, J.S. (Department of Informational Statistics, Kyungsung University) ;
  • Begum, Munni (Department of Mathematical Sciences, Ball State University)
  • Published : 2005.11.30
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Abstract

A nonparametric Bayesian method for calculating posterior probabilities of the multiple comparison problem on the parameters of several Geometric populations is presented. Bayesian multiple comparisons under two different prior/ likelihood combinations was studied by Gopalan and Berry(1998) using Dirichlet process priors. In this paper, we followed the same approach to calculate posterior probabilities for various hypotheses in a statistical experiment with a partition on the parameter space induced by equality and inequality relationships on the parameters of several geometric populations. This also leads to a simple method for obtaining pairwise comparisons of probability of successes. Gibbs sampling technique was used to evaluate the posterior probabilities of all possible hypotheses that are analytically intractable. A numerical example is given to illustrate the procedure.

Keywords

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