Exploratory factor analysis -- parameter estimation and scores prediction with high-dimensional data

DOI10.1016/j.jmva.2016.02.013zbMath1336.62162OpenAlexW2292269400MaRDI QIDQ276966

F. Blanchet-Sadri, M. Dambrine

Publication date: 4 May 2016

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2016.02.013

zbMATH Keywords

SVDfixed point iterationFAEFAfactor score estimationlikelihood equationsmore variables than observations

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25)

Related Items (3)

Adjusting systematic bias in high dimensional principal component scores ⋮ A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data ⋮ A note on the likelihood ratio test in high-dimensional exploratory factor analysis

Uses Software

• R

Cites Work

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• Modelling high-dimensional data by mixtures of factor analyzers
• EM algorithms for ML factor analysis
• Factor analysis and principal components
• Maximum likelihood factor analysis with rank-deficient sample covariance matrices
• A majorization algorithm for simultaneous parameter estimation in robust exploratory factor analysis
• Some contributions to maximum likelihood factor analysis
• Analyzing Microarray Gene Expression Data
• A condition for the regression predictor to be consistent in a single common factor model
• The R Book
• Inferential Theory for Factor Models of Large Dimensions
• VI.—The Estimation of Factor Loadings by the Method of Maximum Likelihood

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