Exact small sample properties of an operational variant of the minimum mean squared error estimator

DOI10.1080/03610929608831760zbMath0875.62111OpenAlexW1990624027MaRDI QIDQ4337187

Kazuhiro Ohtani

Publication date: 5 November 1997

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

Full work available at URL: https://doi.org/10.1080/03610929608831760

zbMATH Keywords

mean squared errorMSEMSE dominanceminimum MSE estimator

Mathematics Subject Classification ID

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

Related Items (12)

Mes performance of the minimum mean squared error estimators in a linear regression model when relevant regressors are omitted ⋮ Risk and Pitman closeness properties of feasible generalized double \(k\)-class estimators in linear regression models with non-spherical disturbances under balanced loss function ⋮ Minimum mean squared error estimation of each individual coefficient in a linear regression model ⋮ An iterative feasible minimum mean squared error estimator of the disturbance variance in linear regression under asymmetric loss ⋮ Estimation of a subset of regression coefficients of interest in a model with non-spherical disturbances ⋮ Shrinkage estimation in spatial autoregressive model ⋮ Goodness of fit for generalized shrinkage estimation ⋮ Minimum mean-squared error estimation in linear regression with an inequality constraint ⋮ MSE performance of the weighted average estimators consisting of shrinkage estimators ⋮ Double \(k\)-class estimators in regression models with non-spherical disturbances ⋮ MSE performance of a heterogeneous pre-test estimator ⋮ An MSE comparison of the restricted Stein-rule and minimum mean squared error estimators in regression

Cites Work

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• Minimum mean square error estimation in linear regression
• Simulation and Extension of a Minimum Mean Squared Error Estimator in Comparison with Stein's
• On the minimum mean squared error estimators in a regression model
• Comparison of operational variants of Best homogeneous and heterogeneous estimators in linear regression

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