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A Shrinkage Predictive Distribution for Multivariate Normal Observables

Fumiyasu Komaki
Biometrika
Vol. 88, No. 3 (Sep., 2001), pp. 859-864
Published by: Oxford University Press on behalf of Biometrika Trust
Stable URL: http://www.jstor.org/stable/2673452
Page Count: 6
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
A Shrinkage Predictive Distribution for Multivariate Normal Observables
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Abstract

We investigate shrinkage methods for constructing predictive distributions. We consider the multivariate Normal model with a known covariance matrix and show that there exists a shrinkage predictive distribution dominating the Bayesian predictive distribution based on the vague prior when the dimension is not less than three. Kullback-Leibler divergence from the true distribution to a predictive distribution is adopted as a loss function.

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