Weighted least-squares estimators of parametric functions of the regression coefficients under a general linear model

DOI10.1007/s10463-008-0199-8zbMath1432.62214OpenAlexW2148270100MaRDI QIDQ907024

Yongge Tian

Publication date: 1 February 2016

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

Full work available at URL: https://doi.org/10.1007/s10463-008-0199-8

zbMATH Keywords

projection matrixparametric functionsgeneral linear regression modelunbiasedness of estimatoruniqueness of estimatorWLSE

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Positive matrices and their generalizations; cones of matrices (15B48)

Related Items (3)

Some overall properties of seemingly unrelated regression models ⋮ On relations between weighted least-squares estimators of parametric functions under a general partitioned linear model and its small models ⋮ The additive and block decompositions about the WLSEs of parametric functions for a multiple partitioned linear regression model

Cites Work

• On \(\mathbf V\)-orthogonal projectors associated with a semi-norm
• Some properties of projectors associated with the WLSE under a general linear model
• On the natural restrictions in the singular Gauss-Markov model
• Projections under seminorms and generalized Moore Penrose inverses
• Characterizations of estimability in the general linear model
• The maximal and minimal ranks of \(A - BXC\) with applications
• The maximal and minimal ranks of some expressions of generalized inverses of matrices
• On constrained generalized inverses of matrices and their properties
• The Restricted Singular Value Decomposition: Properties and Applications
• Rank equalities for idempotent and involutory matrices
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