Estimating correlation matrices that have common eigenvectors.

DOI10.1016/S0167-9473(98)00027-9zbMath1042.62561MaRDI QIDQ1128898

James R. Schott

Publication date: 13 August 1998

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

zbMATH Keywords

Hadamard productPrincipal components analysisCommon principal components

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12)

Related Items (2)

Model-based principal components of correlation matrices ⋮ Estimating common principal components in high dimensions

Cites Work

• Comparison of factor spaces of two related populations
• An approximate test for common principal component subspaces in two groups
• Asymptotic theory for common principal component analysis
• Testing for the equality of several correlation matrices
• A graphical procedure for comparing the principal components of several covariance matrices
• Common principal component subspaces in two groups
• Between-Groups Comparison of Principal Components
• Some tests for common principal component subspaces in several groups
• Asymptotics of eigenprojections of correlation matrices with some applications in principal components analysis
• Two generalizations of the common principal component model
• An Algorithm for Simultaneous Orthogonal Transformation of Several Positive Definite Symmetric Matrices to Nearly Diagonal Form
• Between-group comparison of principal components — some sampling results
• Hadamard matrices and their applications
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Estimating correlation matrices that have common eigenvectors.