Open Access
December, 1991 Bayesian Prediction in Linear Models: Applications to Small Area Estimation
Gauri Sankar Datta, Malay Ghosh
Ann. Statist. 19(4): 1748-1770 (December, 1991). DOI: 10.1214/aos/1176348369


This paper introduces a hierarchical Bayes (HB) approach for prediction in general mixed linear models. The results find application in small area estimation. Our model unifies and extends a number of models previously considered in this area. Computational formulas for obtaining the Bayes predictors and their standard errors are given in the general case. The methods are applied to two actual data sets. Also, in a special case, the HB predictors are shown to possess some interesting frequentist properties.


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Gauri Sankar Datta. Malay Ghosh. "Bayesian Prediction in Linear Models: Applications to Small Area Estimation." Ann. Statist. 19 (4) 1748 - 1770, December, 1991.


Published: December, 1991
First available in Project Euclid: 12 April 2007

zbMATH: 0738.62030
MathSciNet: MR1135147
Digital Object Identifier: 10.1214/aos/1176348369

Primary: 62D05
Secondary: 62F11 , 62F15 , 62J99

Keywords: best linear unbiased prediction , best unbiased prediction , elliptically symmetric distributions , Empirical Bayes , hierarchical Bayes , mixed linear models , nested error regression model , random regression coefficients model , small area estimation , two-stage sampling

Rights: Copyright © 1991 Institute of Mathematical Statistics

Vol.19 • No. 4 • December, 1991
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