Independence distribution preserving covariance structures for the multivariate linear model

DOI10.1006/jmva.1998.1787zbMath0927.62057OpenAlexW2077725959MaRDI QIDQ1283918

John W. jun. Seaman, Dean M. Young, Laurie M. Meaux

Publication date: 26 May 1999

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.1998.1787

zbMATH Keywords

model robustnessWishart random matricesmultivariate quadratic formscommon nonnegative definite solutions

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Matrix equations and identities (15A24)

Related Items (12)

Independence distribution preserving joint covariance structures for the multivariate two-group case ⋮ The general common Hermitian Nonnegative-definite solution to the matrix equationsAXA*=BandCXC*=D ⋮ Characterizations of noncentral chi-squared-generating covariance structures for a normally distributed random vector ⋮ The general common nonnegative-definite and positive-definite solutions to the matrix equations \(AXA^ *=BB^ *\) and \(CXC^ *=DD^ *\). ⋮ The general common Hermitian nonnegative-definite solution to the matrix equations \(AXA=BB\) and \(CXC=DD\) with applications in statistics ⋮ A representation of the general common solution to the matrix equations \(A_1XB_1=C_1\) and \(A_2XB_2=C_2\) with applications ⋮ An optimal approximation problem for a matrix equation ⋮ Common nonnegative definite solutions of some classical matrix equations ⋮ On Hermitian nonnegative-definite solutions to matrix equations ⋮ Full-column rank solutions of the matrix equation \(AV\)=\(EVJ\) ⋮ Nonnegative definite and re-nonnegative definite solutions to a system of matrix equations with statistical applications ⋮ Nonnegative-definite and positive-definite solutions to the matrix equation \(\mathbb{A}\times\mathbb{A}^*=\mathbb{B}\) -- revisited

Cites Work

• On the robustness of least squares procedures in regression models
• Multivariate versions of Cochran's theorems
• Nonnegative definite and positive definite solutions to the matrix equationAXA^*=B
• A sufficient condition on the covariance matrix for F tests in linear models to be valid
• When Does Rank(A+B)=Rank(A)+Rank(B)?
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