Linear Bayes and minimax estimation in linear models with partially restricted parameter space

DOI10.1016/0378-3758(93)90122-MzbMath0780.62027OpenAlexW2011803211MaRDI QIDQ689385

Berthold Heiligers

Publication date: 6 January 1994

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0378-3758(93)90122-m

zbMATH Keywords

mean squared errordualityBayes estimationlinear modelsdecomposition result for Bayes estimatorsellipsoidal restrictionsfull parameter restrictionsleast favourable prior distributionslinear minimax estimationpartial parameter restrictions

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Point estimation (62F10) Parametric inference under constraints (62F30) Bayesian inference (62F15) Minimax procedures in statistical decision theory (62C20)

Related Items (7)

Estimation using the linear regression model with incomplete ellipsoidal restrictions ⋮ Fuzzy prior information and minimax estimation in the linear regression model ⋮ Linear sufficiency and admissibility in restricted linear models ⋮ Linear Bayes estimators applied to the inverse Gaussian lifetime model ⋮ On estimation of variance components with constraints ⋮ Minimax estimation in linear regression under restrictions ⋮ Unnamed Item

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• A minimax linear estimator for linear parameters under restrictions in form of inequalities
• Admissibility of linear estimators with respect to restricted parameter sets
• Bayes, Admissible, and Minimax Linear Estimators in Linear Models with Restricted Parameter Space

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