Uporedite metode

	Робусна канонска корелациона анализа (Robust CCA)	Робусна експлоратарна факторска анализа
Oblast	Statistika	Psihometrija
Porodica	Latent structure	Latent structure
Godina nastanka	2003	2000–2003
Tvorac	Croux & Dehon (building on Hotelling's CCA framework)	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)
Tip	Robust multivariate association	Latent variable / dimension reduction (robust)
Temeljni izvor	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.
Drugi nazivi	Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation
Srodne	4	4
Sažetak	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.