A subclass of Bayes linear estimators that are minimax

DOI10.1007/BF00046990zbMath0844.62008OpenAlexW2051604121MaRDI QIDQ1914886

Kurt Hoffmann

Publication date: 29 August 1996

Published in: Acta Applicandae Mathematicae (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00046990

zbMATH Keywords

least squares estimatorrestricted parameter spaceuniversal admissibilityBayes linear estimatorsellipsoidal parameter constraintsgeneralized quadratic risk function

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian problems; characterization of Bayes procedures (62C10) Minimax procedures in statistical decision theory (62C20)

Related Items (2)

Median loss decision theory ⋮ Computing the estimator of a parameter vector via a competing Bayes method

Cites Work

• Minimax linear regression estimation with symmetric parameter restrictions
• Full and partial minimax estimation in regression analysis with additional linear constraints
• Estimation of parameters in a linear model
• Characterization of minimax linear estimators in linear regression
• Note on a paper
• Minimax estimation with additional linear restrictions - a simulation study
• Quasi minimax estimation in the linear regression model
• Minimax properties for linear estimators of the location parameter of a linear model
• Admissible improvements of the least squares estimator¹
• Improved inference in linear models with additional information
• Minimax linear, ridge and shrunken estimators for linear parameters
• A minimax linear estimator for linear parameters under restrictions in form of inequalities
• Bayes, Admissible, and Minimax Linear Estimators in Linear Models with Restricted Parameter Space

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