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April 2006 Shrinkage priors for Bayesian prediction
Fumiyasu Komaki
Ann. Statist. 34(2): 808-819 (April 2006). DOI: 10.1214/009053606000000010


We investigate shrinkage priors for constructing Bayesian predictive distributions. It is shown that there exist shrinkage predictive distributions asymptotically dominating Bayesian predictive distributions based on the Jeffreys prior or other vague priors if the model manifold satisfies some differential geometric conditions. Kullback–Leibler divergence from the true distribution to a predictive distribution is adopted as a loss function. Conformal transformations of model manifolds corresponding to vague priors are introduced. We show several examples where shrinkage predictive distributions dominate Bayesian predictive distributions based on vague priors.


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Fumiyasu Komaki. "Shrinkage priors for Bayesian prediction." Ann. Statist. 34 (2) 808 - 819, April 2006.


Published: April 2006
First available in Project Euclid: 27 June 2006

zbMATH: 1092.62037
MathSciNet: MR2283393
Digital Object Identifier: 10.1214/009053606000000010

Primary: 62C15 , 62F15

Keywords: Asymptotic theory , conformal transformation , information geometry , Jeffreys prior , Kullback–Leibler divergence , vague prior

Rights: Copyright © 2006 Institute of Mathematical Statistics

Vol.34 • No. 2 • April 2006
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