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Biometrika 1996 83(2):275-285; doi:10.1093/biomet/83.2.275
© 1996 by Biometrika Trust
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A semiparametric Bayesian model for randomised block designs

CHRISTOPHER A. BUSH1 and STEVEN N. MACEACHERN2

1Ross Products Division, Abbott Laboratories, Department 105620 RP3-2 625 Cleveland Avenue, Columbus, Ohio 43215-1724, U.S.A.
2Department of Statistics, The Ohio State University Columbus, Ohio 43210, U.S.A.

A model is proposed for a Bayesian semiparametric analysis of randomised block experiments. The model is a hierarchical model in which a Dirichlet process is inserted at the middle stage for the distribution of the block effects. This model allows an arbitrary distribution of block effects, and it results in effective estimates of treatment contrasts, block effects and the distribution of block effects. An effective computational strategy is presented for describing the posterior distribution.

Key Words: Dirichlet process • Gibbs sampler • Hierarchical model


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