Generalized Bayes minimax estimation of the normal mean matrix with unknown covariance matrix

DOI10.1016/j.jmva.2009.04.009zbMath1175.62006OpenAlexW2024554937MaRDI QIDQ1036793

Hisayuki Tsukuma

Publication date: 13 November 2009

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2009.04.009

zbMATH Keywords

decision theoryshrinkage estimatorminimaxityequivariancehierarchical modelposterior meanmultivariate linear modelquadratic lossgeneralized Bayes estimation

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Bayesian inference (62F15) Bayesian problems; characterization of Bayes procedures (62C10) Minimax procedures in statistical decision theory (62C20)

Related Items

Bayes minimax estimation of the mean matrix of matrix-variate normal distribution under balanced loss function ⋮ Generalized Bayes estimators with closed forms for the normal mean and covariance matrices ⋮ Shrinkage estimation with a matrix loss function ⋮ Matrix linear minimax estimators in a general multivariate linear model under a balanced loss function ⋮ Hierarchical multilinear models for multiway data ⋮ Generalized ridge estimator and model selection criteria in multivariate linear regression ⋮ Shrinkage priors for Bayesian estimation of the mean matrix in an elliptically contoured distribution ⋮ Weighted shrinkage estimators of normal mean matrices and dominance properties

Cites Work

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• A new class of generalized Bayes minimax ridge regression estimators
• Empirical Bayes on vector observations: An extension of Stein's method

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