Raw data maximum likelihood estimation for common principal component models: a state space approach

DOI10.1007/s11336-016-9504-2zbMath1345.62147OpenAlexW2462095600WikidataQ31111620 ScholiaQ31111620MaRDI QIDQ316724

Mohammad Hasan, M. Dambrine

Publication date: 27 September 2016

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-016-9504-2

zbMATH Keywords

principal component analysisstate space modelcommon principal component model

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to psychology (62P15)

Uses Software

• R
• LISREL
• OpenMx
• AS 211

Cites Work

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• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
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• Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences
• Stepwise estimation of common principal components
• The Efficiency of the Asymptotic Expansion of the Distribution of the Canonical Vector under Nonnormality
• Common Canonical Variates in κ Independent Groups
• Two generalizations of the common principal component model
• An Algorithm for Simultaneous Orthogonal Transformation of Several Positive Definite Symmetric Matrices to Nearly Diagonal Form
• Maximum likelihood estimation of parameters in multivariate Gaussian stochastic processes (Corresp.)
• Common canonical variates
• Hierarchical relations between methods for simultaneous component analysis and a technique for rotation to a simple simultaneous structure
• Evaluation of likelihood functions for Gaussian signals
• Robustness of normal theory methods in the analysis of linear latent variate models

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