Сравнение методов

	Робастный канонический корреляционный анализ (Robust CCA)	Робастный эксплораторный факторный анализ
Область	Статистика	Психометрия
Семейство	Latent structure	Latent structure
Год появления	2003	2000–2003
Автор метода	Croux & Dehon (building on Hotelling's CCA framework)	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)
Тип	Robust multivariate association	Latent variable / dimension reduction (robust)
Основополагающий источник	Croux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569.	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.
Другие названия	Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation
Связанные	4	4
Сводка	Robust canonical correlation analysis extends classical CCA by replacing the standard sample covariance matrix with a robust estimator — such as the Minimum Covariance Determinant (MCD) or S-estimator — so that outlying observations do not distort the estimated canonical correlations and canonical variates between two sets of variables.	Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such as minimum covariance determinant or iteratively reweighted least squares — so that a small fraction of atypical cases cannot distort the recovered factor loadings.