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Operational variants of the minimum mean squared error estimator in linear regression models with non-spherical disturbances

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Publication:1585891
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DOI10.1023/A:1004169923370zbMath1064.62542OpenAlexW1975145240MaRDI QIDQ1585891

Alan T. K. Wan, Anoop Chaturvedi

Publication date: 8 January 2003

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1004169923370



Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Monte Carlo methods (65C05)


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Feasible generalized Stein-rule restricted ridge regression estimators ⋮ STEIN-RULE RESTRICTED REGRESSION ESTIMATOR IN A LINEAR REGRESSION MODEL WITH NONSPHERICAL DISTURBANCES ⋮ SEPARATE VERSUS SYSTEM METHODS OF STEIN-RULE ESTIMATION IN SEEMINGLY UNRELATED REGRESSION MODELS ⋮ 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 ⋮ Improved multivariate prediction in a general linear model with an unknown error covariance matrix.



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