Empirical Bayes analysis of log-linear models for a generalized finite stationary Markov chain

Farzad Eskandari () and Mohammad R. Meshkani

Metrika: International Journal for Theoretical and Applied Statistics, 2004, vol. 59, issue 2, 173-191

Abstract: This article presents the empirical Bayes method for estimation of the transition probabilities of a generalized finite stationary Markov chain whose ith state is a multi-way contingency table. We use a log-linear model to describe the relationship between factors in each state. The prior knowledge about the main effects and interactions will be described by a conjugate prior. Following the Bayesian paradigm, the Bayes and empirical Bayes estimators relative to various loss functions are obtained. These procedures are illustrated by a real example. Finally, asymptotic normality of the empirical Bayes estimators are established. Copyright Springer-Verlag 2004

Keywords: Log-linear models; Multinomial distribution; Finite stationary Markov chain; Bayes; Empirical Bayes; Model Selection; Panel data (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:spr:metrik:v:59:y:2004:i:2:p:173-191

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DOI: 10.1007/s001840300278

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