Application of Stein-type estimation in combining regression estimates from replicated experiments

DOI10.1007/BF02926404zbMath0807.62053OpenAlexW1968947871MaRDI QIDQ1342789

Virendra K. Srivastava, Helge Toutenbourg

Publication date: 1 March 1995

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02926404

zbMATH Keywords

regression coefficientsmean squared error matrix comparisonconvex combination of least squares estimatorsStein- type shrinkage procedureweighted mean squared error

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (3)

Pitman nearness comparisons of Stein-type estimators for regression coefficients in replicated experiments ⋮ Parameter estimation in linear models with heteroscedastic variances subject to order restrictions ⋮ Prediction of response values in linear regression models from replicated experiments

Cites Work

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• A necessary and sufficient condition for the dominance of an improved family of estimators in linear regression models
• On comparing restricted least squares estimators
• Mean squared error matrix comparisons between biased estimators — An overview of recent results
• A mean square error test when stochastic restrictions are used in regression

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