Minimax estimators in the MANOVA model for arbitrary quadratic loss and unknown covariance matrix

DOI10.1016/0047-259X(91)90095-JzbMath0725.62013OpenAlexW2073521505MaRDI QIDQ2277697

Toshio Honda

Publication date: 1991

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

Full work available at URL: https://doi.org/10.1016/0047-259x(91)90095-j

zbMATH Keywords

Stein's identityMANOVA modelnormal multivariate regression modelStein type estimatorGleser type estimatorsGleser's methodHaff's identityunbiased estimators of risk matrix difference

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Minimax procedures in statistical decision theory (62C20)

Related Items (6)

On multivariate linear regression shrinkage and reduced-rank procedures ⋮ Biased estimation in a simple multivariate regression model ⋮ Robust improvement in estimation of a mean matrix in an elliptically contoured distribution ⋮ Admissibility and inadmissibility of a generalized Bayes unbiased estimator in a multivariate linear model ⋮ Construdtion of shrinkage estimators for the regression coefficient matrix in the gmanova model ⋮ Shrinkage to smooth non-convex cone :Principal component analysis as stein estimation

Cites Work

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• Minimax estimation of a normal mean vector when the covariance matrix is unknown
• An identity for the Wishart distribution with applications
• Estimation of the mean of a multivariate normal distribution
• Minimax estimation of a normal mean vector for arbitrary quadratic loss and unknown covariance matrix
• Minimax estimates of a normal mean vector for arbitrary quadratic loss and unknown covariance matrix
• Minimax estimators in the normal MANOVA model

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