Characterization of general ridge estimators

DOI10.1016/0167-7152(95)00056-9zbMath0849.62036OpenAlexW2032287644MaRDI QIDQ1916181

Augustyn Markiewicz

Publication date: 2 July 1996

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

Full work available at URL: https://doi.org/10.1016/0167-7152(95)00056-9

zbMATH Keywords

linear sufficiencygeneral ridge estimatorsnonsingular linear modelset of all linear estimators

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Admissibility in statistical decision theory (62C15)

Related Items (14)

Characterizations of admissible linear estimators in the linear model ⋮ Local improvement of best linear unbiased estimation and admissibility under the weakly singular gauss-markov model ⋮ General ridge predictors in a mixed linear model ⋮ Linear sufficiency and admissibility in restricted linear models ⋮ Admissible linear estimators in the general Gauss-Markov model under generalized extended balanced loss function ⋮ Linear sufficiency and linear admissibility in a continuous time Gauss-Markov model. ⋮ Some properties of the relative squared error approach to linear regression analysis ⋮ Linear completeness in a continuous time Gauss-Markov model ⋮ Comparison of linear restricted models with respect to the validity of admissible and linearly sufficient estimators ⋮ Covariance structure associated with an equality between two general ridge estimators ⋮ Another view of the Kuks-Olman estimator ⋮ On contractions in linear regression ⋮ Sufficient and admissible estimators in general multivariate linear model ⋮ Linear regression analysis using the relative squared error

Cites Work

• Unnamed Item
• Admissible linear estimators in restricted linear models
• Characterizations of admissible linear estimators in restricted linear models
• Admissible linear estimators in the general Gauss-Markov model
• Linear transformations preserving best linear unbiased estimators in a general Gauss-Markoff model
• Estimation of parameters in a linear model
• Sufficiency and completeness in the linear model
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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