A note on the equality of the OLSE and the BLUE of the parametric function in the general Gauss-Markov model

DOI10.1007/s00362-007-0055-6zbMath1309.62113OpenAlexW1988439727MaRDI QIDQ1019437

Simo Puntanen, Jarkko Isotalo

Publication date: 2 June 2009

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

Full work available at URL: https://doi.org/10.1007/s00362-007-0055-6

zbMATH Keywords

linear modelbest linear unbiased estimationordinary least squares estimationreparametrized model

Mathematics Subject Classification ID

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

Related Items (12)

On connections among OLSEs and BLUEs of whole and partial parameters under a general linear model ⋮ On the equality of estimators under a general partitioned linear model with parameter restrictions ⋮ On relations between weighted least-squares estimators of parametric functions under a general partitioned linear model and its small models ⋮ On equalities of estimations of parametric functions under a general linear model and its restricted models ⋮ Equalities for estimators of partial parameters under linear model with restrictions ⋮ Some equalities for estimations of partial coefficients under a general linear regression model ⋮ The additive and block decompositions about the WLSEs of parametric functions for a multiple partitioned linear regression model ⋮ Matrix rank and inertia formulas in the analysis of general linear models ⋮ Perfect linear models and perfect parametric functions ⋮ On the BLUEs in Two Linear Models via C. R. Rao's Pandora's Box ⋮ The equalities of estimations under a general partitioned linear model and its stochastically restricted model ⋮ Mean driven balance and uniformly best linear unbiased estimators

Cites Work

• Representations of best linear unbiased estimators in the Gauss-Markoff model with a singular dispersion matrix
• Four equivalent definitions of reparametrizations and restrictions in linear models
• On Canonical Forms, Non-Negative Covariance Matrices and Best and Simple Least Squares Linear Estimators in Linear Models
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