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http://dx.doi.org/10.5351/KJAS.2003.16.1.101

Semiparametric Bayesian Hierarchical Selection Models with Skewed Elliptical Distribution  

정윤식 (부산대학교 자연과학대학 통계학과)
장정훈 (부산대학교 자연과학대학 통계학과)
Publication Information
The Korean Journal of Applied Statistics / v.16, no.1, 2003 , pp. 101-115 More about this Journal
Abstract
Lately there has been much theoretical and applied interest in linear models with non-normal heavy tailed error distributions. Starting Zellner(1976)'s study, many authors have explored the consequences of non-normality and heavy-tailed error distributions. We consider hierarchical models including selection models under a skewed heavy-tailed e..o. distribution proposed originally by Chen, Dey and Shao(1999) and Branco and Dey(2001) with Dirichlet process prior(Ferguson, 1973) in order to use a meta-analysis. A general calss of skewed elliptical distribution is reviewed and developed. Also, we consider the detail computational scheme under skew normal and skew t distribution using MCMC method. Finally, we introduce one example from Johnson(1993)'s real data and apply our proposed methodology.
Keywords
Bayesian meta analysis; Density generator function; Dirichlet process prior; Gibbs sampler; MCMC method; Skew elliptical distribution; Weighted function;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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