Abstract
We investigate Bayesian shrinkage methods for constructing predictive distributions. We consider the multivariate normal model with a known covariance matrix and show that the Bayesian predictive density with respect to Stein’s harmonic prior dominates the best invariant Bayesian predictive density when the dimension is greater than or equal to 3. Alpha divergence from the true distribution to a predictive distribution is adopted as a loss function.
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