Bayesian predictive densities based on superharmonic priors for the 2-dimensional Wishart model
Bayesian predictive densities for the 2-dimensional Wishart model are investigated. The performance of predictive densities is evaluated by using the Kullback-Leibler divergence. It is proved that a Bayesian predictive density based on a prior exactly dominates that based on the Jeffreys prior if the prior density satisfies some geometric conditions. An orthogonally invariant prior is introduced and it is shown that the Bayesian predictive density based on the prior is minimax and dominates that based on the right invariant prior with respect to the triangular group.
Year of publication: |
2009
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Authors: | Komaki, Fumiyasu |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 100.2009, 10, p. 2137-2154
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Publisher: |
Elsevier |
Keywords: | Differential geometry Green's theorem Group models Jeffreys prior Kullback-Leibler divergence Minimaxity Orthogonally invariant priors Right invariant prior |
Saved in:
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