Sankhya: The Indian Journal of Statistics

1997, Volume 59, Series B, Pt. 3, 326-334

HIERARCHICAL BAYESIAN ANALYSIS OF LONGITUDINAL DATA

By

MALAY GHOSH, University of Florida, Gainesville

DAL HO KIM, Kyungpook National University

And

TAPABRATA MAITI, University of Florida, Gainesville

SUMMARY. In this paper, we consider hierarchical Bayes generalized linear models for the analysis of longitudinal data. Specifically, we introduce the hierarchical Bayes random effects models and find sufficient conditions for the propriety of posteriors under noninformative priors. We discuss also implementation of the Bayes procedure via Markov Chain Monte Carlo integration techniques. The hierarchical Bayes method is illustrated with a real dataset and is compared with the corresponding empirical Bayes analysis of Waclawiw and Liang (1994).

AMS (1991) subject classification. 62F15, 62D05

Key words and phrases. Random effects models, hierarchical Bayes, empirical Bayes, longitudinal data, Gibbs sampling

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