Common principal components for dependent random vectors

DOI10.1006/jmva.2000.1908zbMath0961.62055OpenAlexW2008157510WikidataQ118992557 ScholiaQ118992557MaRDI QIDQ1840776

Beat E. Neuenschwander, Bernhard K. Flury

Publication date: 5 June 2001

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

Full work available at URL: https://doi.org/10.1006/jmva.2000.1908

zbMATH Keywords

entropyeigenvalueeigenvectornormal distributionmaximum likelihood estimatorspatterned covariance matrices

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12)

Related Items (3)

Raw data maximum likelihood estimation for common principal component models: a state space approach ⋮ A 50-year personal journey through time with principal component analysis ⋮ Common principal components for dependent random vectors

Cites Work

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• Biased Estimation in Regression: An Evaluation Using Mean Squared Error
• Principal Curves
• Common canonical variates
• Maximum Entropy for Hypothesis Formulation, Especially for Multidimensional Contingency Tables
• Asymptotic Theory for Principal Component Analysis

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