Some further remarks on the singular linear model

DOI10.1016/0024-3795(95)00560-9zbMath0843.62069OpenAlexW2204208920MaRDI QIDQ1914236

Simo Puntanen, Alastair J. Scott

Publication date: 21 August 1996

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0024-3795(95)00560-9

zbMATH Keywords

singularityinvariance propertiesgeneralized inversesbest linear unbiased estimatorgeneral linear model

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Linear inference, regression (62J99) Theory of matrix inversion and generalized inverses (15A09)

Related Items (max. 100)

Some overall properties of seemingly unrelated regression models ⋮ The general Gauss-Markov model with possibly singular dispersion matrix ⋮ Comparing the BLUEs Under Two Linear Models ⋮ Linear models that allow perfect estimation ⋮ On the natural restrictions in the singular Gauss-Markov model ⋮ On consistency, natural restrictions and estimability under classical and extended growth curve models ⋮ A Useful Matrix Decomposition and Its Statistical Applications in Linear Regression

Cites Work

• On parallel summability of matrices
• Unbiased and minimum-variance unbiased estimation of estimable functions for fixed linear models with arbitrary covariance structure
• A study of the influence of the natural restrictions on estimation problems in the singular Gauss-Markov model
• Extending some results and proofs for the singular linear model
• Representations of best linear unbiased estimators in the Gauss-Markoff model with a singular dispersion matrix
• Categorical information and the singular linear model
• IV.—On Least Squares and Linear Combination of Observations
• Weak generalized inverses and minimum variance linear unbiased estimation
• On Canonical Forms, Non-Negative Covariance Matrices and Best and Simple Least Squares Linear Estimators in Linear Models
• On Best Linear Estimation and General Gauss-Markov Theorem in Linear Models with Arbitrary Nonnegative Covariance Structure
• Linear Spaces and Minimum Variance Unbiased Estimation
• The Gauss–Markov Theorem for Regression Models with Possibly Singular Covariances
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