An approach to improving the James-Stein estimator

DOI10.1016/0047-259X(91)90096-KzbMath0733.62059OpenAlexW1989648234MaRDI QIDQ809506

Tatsuya Kubokawa

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)90096-k

zbMATH Keywords

James-Stein estimatornormal distributioninadmissibilityquadratic lossgeneralized Bayes estimatorsmean vectornormal variance

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Admissibility in statistical decision theory (62C15)

Related Items (17)

SHRINKAGE EFFICIENCY BOUNDS ⋮ Further improving the Stein-rule estimator using the Stein variance estimator in a misspecified linear regression model ⋮ Optimization of ridge parameters in multivariate generalized ridge regression by plug-in methods ⋮ Bayesian inference and prediction for mean-mixtures of normal distributions ⋮ Improvements over the james-stein estimator: A risk analysis ⋮ Stein's idea and minimax admissible estimation of a multivariate normal mean ⋮ Estimation in a linear regression model under the Kullback-Leibler loss and its application to model selection ⋮ Generalized Bayes estimators of a normal discriminant function ⋮ Applications of Improved Variance Estimators in a Multivariate Normal Mean Vector Estimation ⋮ A sequence of improvements over the James-Stein estimator ⋮ Stein estimation -- a review ⋮ An Appreciation of Balanced Loss Functions Via Regret Loss ⋮ A Note on Estimation of a Distribution Function in a Nonparametric Set-up Using Stein’s Shrinkage Estimation Technique ⋮ Estimation Of A Multivariate Normal Mean Vector And Local Improvements ⋮ On admissible estimation of a mean vector when the scale is unknown ⋮ Shrinkage and modification techniques in estimation of variance and the related problems: A review ⋮ Necessary conditions for dominating the James-Stein estimator

Cites Work

• Admissible minimax estimation of a multivariate normal mean with arbitrary quadratic loss
• Improving on equivariant estimators
• Generalized Bayes minimax estimators of the multivariate normal mean with unknown covariance matrix
• Inadmissibility of the Usual Estimators of Scale parameters in Problems with Unknown Location and Scale Parameters
• Proper Bayes Minimax Estimators of the Multivariate Normal Mean
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