Controlled shrinkage estimators (a class of estimators better than the least squares estimator, with respect to a general quadratic loss, for normal observations

DOI10.1080/02331888908802138zbMath0669.62035OpenAlexW1989890690MaRDI QIDQ3823000

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Publication date: 1989

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888908802138

zbMATH Keywords

linear modelmaximum likelihood estimatornormal vectorshrinkage estimatorsquadratic lossfinite-dimensional real vector spaceJames-Stein estimatorsuniform dominationshrinkage functions

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Minimax procedures in statistical decision theory (62C20)

Related Items (1)

An application of a minimax Bayes rule and shrinkage estimators to the portfolio selection problem under the Bayesian approach

Cites Work

• A robust generalized Bayes estimator and confidence region for a multivariate normal mean
• Estimation of the mean of a multivariate normal distribution
• Minimax estimators of the mean of a multivariate normal distribution
• Admissible minimax estimation of a multivariate normal mean with arbitrary quadratic loss
• Proper Bayes minimax estimators of the multivariate normal mean vector for the case of common unknown variances
• A family of admissible minimax estimators of the mean of a multivariate normal distribution
• Estimation with Incompletely Specified Loss Functions (the Case of Several Location Parameters)
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