한국데이터정보과학회:학술대회논문집
- 한국데이터정보과학회 2006년도 추계 학술발표회 논문집
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- Pages.71-80
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- 2006
Nonparametric Bayesian Multiple Comparisons for Dependence Parameter in Bivariate Exponential Populations
- 조장식 (경성대학교) ;
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- Cho, Jang-Sik (Department of Information Statistics, Kyungsung University) ;
- Ali, M. Masoom (Department of Mathematical Science, Ball State University) ;
- Begum, Munni (Department of Mathematical Science, Ball State Univeristy)
- 발행 : 2006.11.17
초록
A nonparametric Bayesian multiple comparisons problem (MCP) for dependence parameters in I bivariate exponential populations is studied here. A simple method for pairwise comparisons of these parameters is also suggested. Here we extend the methodology studied by Gopalan and Berry (1998) using Dirichlet process priors. The family of Dirichlet process priors is applied in the form of baseline prior and likelihood combination to provide the comparisons. Computation of the posterior probabilities of all possible hypotheses are carried out through Markov Chain Monte Carlo method, namely, Gibbs sampling, due to the intractability of analytic evaluation. The whole process of MCP for the dependent parameters of bivariate exponential populations is illustrated through a numerical example.
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