A shrinkage predictive distribution for multivariate Normal observables

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Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models (2008, methodology): “·| In terms of expected Kullback– Leibler divergence, the naive plug-in predictor density ξ ( ˆ β, ˆ ψ ) performs poorly compared to Bayesian predictors ξ ( β, ψ ) π ( β, ψ Y n ), see for example, Aitchison (1975), Murray (1977), Ng (1980), Komaki (1996, 2001, 2006) and George, Liang and Xu (2006).”
Shrinkage priors for Bayesian prediction (2006, methodology): “The explicit form pπG(y|x) for the d-dimensional Normal model was obtained and it was shown that pπG(y|x) exactly dominates pπJ(y|x) for arbitrary N > 0; see [19].”
Admissible predictive density estimation (2008, methodology): “μπ(μ|x)dμ under the harmonic prior when p ≥ 3 [Stein (1974)] and by the proper Bayes rule under the Strawderman priors when p≥ 5 [Strawderman (1971)].”