Komaki, Fumiyasu A shrinkage predictive distribution for multivariate normal observables. (English) Zbl 0985.62024 Biometrika 88, No. 3, 859-864 (2001). Summary: 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. Cited in 1 ReviewCited in 47 Documents MSC: 62F15 Bayesian inference 62J07 Ridge regression; shrinkage estimators (Lasso) Keywords:invariance; James-Stein estimator; Stein’s prior; vague prior; Kullback-Leibler divergence PDFBibTeX XMLCite \textit{F. Komaki}, Biometrika 88, No. 3, 859--864 (2001; Zbl 0985.62024) Full Text: DOI