Сравнение методов

	Робастный канонический корреляционный анализ (Robust CCA)	Канонический корреляционный анализ
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	2003	1936
Автор метода	Croux & Dehon (building on Hotelling's CCA framework)	Harold Hotelling
Тип	Robust multivariate association	Multivariate linear dimension reduction and association
Основополагающий источник	Croux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569.	Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377.
Другие названия	Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation	CCA, canonical variate analysis, canonical analysis, multiple canonical correlation
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.	Canonical Correlation Analysis (CCA) is a multivariate statistical method that identifies pairs of linear combinations — one from each of two variable sets — such that the correlation between each pair is maximised. Introduced by Harold Hotelling in his landmark 1936 Biometrika paper, CCA provides the most general linear framework for studying the association between two multivariate batteries of measurements, and many classical procedures (multiple regression, MANOVA, discriminant analysis) are special cases of it.