A Note on Superiority Comparisons of Homogeneous Linear Estimators

DOI10.1080/03610928308828496zbMath0523.62066OpenAlexW2113584409MaRDI QIDQ3673895

Dietrich Trenkler, Götz Trenkler

Publication date: 1983

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610928308828496

zbMATH Keywords

generalized least squares estimatorbiased estimationminimum variance criterioncomparisons of homogeneous linear estimatorsminimum average risk linear estimatorminimum conditional mean square error estimatorminimum risk estimators

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Point estimation (62F10)

Related Items (10)

Minimax linear regression estimation with symmetric parameter restrictions ⋮ Mean square error matrix comparisons of estimators in linear regression ⋮ Some remarks on a ridge-type-estimator and good prior means ⋮ Mean squared error matrix comparisons between biased estimators — An overview of recent results ⋮ A generalization and Bayesian interpretation of ridge-type estimators with good prior means ⋮ Quasi minimax estimation in the linear regression model ⋮ On a principal component two-parameter estimator in linear model with autocorrelated errors ⋮ Superiority comparisons of heterogeneous linear estimators ⋮ On the performance of biased estimators in the linear regression model with correlated or heteroscedastic errors ⋮ \(r\)-\(k\) class estimator in the linear regression model with correlated errors

Cites Work

• Good and optimal ridge estimators
• Über einige verzerrte Schätzer für die Koeffizienten eines linearen Modells
• Minimum average risk estimators for coefficients in linear models
• A note on minimum average risk estimators for coefficients in linear models
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