Võrdle meetodeid

	Robustne kanooniline korrelatsioonianalüüs (Robust CCA)	Robustne diskriminantanalüüs
Valdkond	Statistika	Statistika
Perekond	Latent structure	Regression model
Tekkeaasta	2003	1997
Looja	Croux & Dehon (building on Hotelling's CCA framework)	Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)
Tüüp	Robust multivariate association	Robust classification / discriminant analysis
Algallikas	Croux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569.	Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143.
Rööpnimetused	Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation	robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi
Seotud	4	5
Kokkuvõte	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 Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such as the Minimum Covariance Determinant (MCD), an approach developed by Hawkins & McLachlan (1997) and Croux & Dehon (2001).