Linear sufficiency and admissibility in restricted linear models

DOI10.1016/0167-7152(95)00240-5zbMath0903.62060OpenAlexW2048525336MaRDI QIDQ1126104

Berthold Heiligers, Augustyn Markiewicz

Publication date: 10 January 1999

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(95)00240-5

zbMATH Keywords

linear sufficiencypartial parameter restrictionsgeneral ridge estimatorsmean squared error function

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Point estimation (62F10) Admissibility in statistical decision theory (62C15) Sufficient statistics and fields (62B05)

Related Items (13)

Characterizations of admissible linear estimators in the linear model ⋮ On relations between BLUPs under two transformed linear random-effects models ⋮ Quadratic prediction and quadratic sufficiency in finite populations ⋮ Fundamental Equations of BLUE and BLUP in the Multivariate Linear Model with Applications ⋮ Predictions and estimations under a group of linear models with random coefficients ⋮ Linear sufficiency in a general growth curve model ⋮ Transformation approaches of linear random-effects models ⋮ Best linear unbiased prediction for linear combinations in general mixed linear models ⋮ Best Quadratic Unbiased Prediction in a General Linear Model with Stochastic Regression Coefficients ⋮ Admissible Estimation for Linear Combination of Fixed and Random Effects in General Mixed Linear Models ⋮ Nonnegative quadratic estimation and quadratic sufficiency in general linear models ⋮ Comparison of linear restricted models with respect to the validity of admissible and linearly sufficient estimators ⋮ Sufficient and admissible estimators in general multivariate linear model

Cites Work

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• Linear Bayes and minimax estimation in linear models with partially restricted parameter space
• Linear transformations preserving best linear unbiased estimators in a general Gauss-Markoff model
• Estimation of parameters in a linear model
• Characterization of general ridge estimators
• 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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