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Robust Bayesian meta analysis  

Choi, Seong-Mi (Department of Statistics, Kyungpook National University)
Kim, Dal-Ho (Department of Statistics, Kyungpook National University)
Shin, Im-Hee (Department of Medical Statistics, School of Medicine, Catholic University of Daegu)
Kim, Ho-Gak (Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Catholic University of Daegu)
Kim, Sang-Gyung (Department of Laboratory Medicine, School of Medicine, Catholic University of Daegu)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.3, 2011 , pp. 459-466 More about this Journal
Abstract
This article addresses robust Bayesian modeling for meta analysis which derives general conclusion by combining independently performed individual studies. Specifically, we propose hierarchical Bayesian models with unknown variances for meta analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. For the numerical analysis, we use the Gibbs sampler for calculating Bayesian estimators and illustrate the proposed methods using actual data.
Keywords
Gibbs sampling; hierarchical Bayesain; meta-analysis; normal; robust; scale mixture;
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