Proper Bayes and minimax predictive densities related to estimation of a normal mean matrix

DOI10.1016/j.jmva.2017.05.004zbMath1368.62017OpenAlexW2620075508MaRDI QIDQ2011522

Tatsuya Kubokawa, Hisayuki Tsukuma

Publication date: 3 August 2017

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2017.05.004

zbMATH Keywords

shrinkage estimatorminimaxityinadmissibilitygeneralized Bayes estimatoradmissibilitystatistical decision theoryKullback-Leibler lossGauss' divergence theorem

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Bayesian problems; characterization of Bayes procedures (62C10) Minimax procedures in statistical decision theory (62C20) Admissibility in statistical decision theory (62C15)

Related Items

Generalized Bayes estimators with closed forms for the normal mean and covariance matrices ⋮ Matrix variate density estimation with additional information ⋮ Density prediction and the Stein phenomenon ⋮ Proper Bayes and minimax predictive densities related to estimation of a normal mean matrix

Cites Work

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• Admissible predictive density estimation
• Admissibility and minimaxity of Bayes estimators for a normal mean matrix
• Selecting a minimax estimator doing well at a point
• Estimation of the mean of a multivariate normal distribution
• Multivariate empirical Bayes and estimation of covariance matrices
• Minimax Bayes estimators of a multivariate normal mean
• A unified and broadened class of admissible minimax estimators of a multivariate normal mean
• On estimation of matrix of normal mean
• Proper Bayes and minimax predictive densities related to estimation of a normal mean matrix
• Posterior propriety and admissibiity of hyperpriors in normal hierarchical models
• Improved minimax predictive densities under Kullback-Leibler loss
• From minimax shrinkage estimation to minimax shrinkage prediction
• A shrinkage predictive distribution for multivariate Normal observables
• Singular value shrinkage priors for Bayesian prediction
• Goodness of prediction fit
• Proper Bayes Minimax Estimators of the Multivariate Normal Mean
• Empirical Bayes on vector observations: An extension of Stein's method
• Admissible Estimators, Recurrent Diffusions, and Insoluble Boundary Value Problems

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