Bayesian prediction based on a class of shrinkage priors for location-scale models

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Abstract

A class of shrinkage priors for multivariate location-scale models is introduced. We consider Bayesian predictive densities for location-scale models and evaluate performance of them using the Kullback–Leibler divergence. We show that Bayesian predictive densities based on priors in the introduced class asymptotically dominate the best invariant predictive density.

Keywords

Asymptotic theory Jeffreys prior Neyman–Scott model Right invariant prior Kullback–Leibler divergence 
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Copyright information

© The Institute of Statistical Mathematics, Tokyo 2007

Authors and Affiliations

  1. 1.Department of Mathematical Informatics, Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan

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