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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse en composantes canoniques robuste (ACCR robuste)× | Analyse canonique des corrélations× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Latent structure | Latent structure |
| Année d'origine≠ | 2003 | 1936 |
| Auteur d'origine≠ | Croux & Dehon (building on Hotelling's CCA framework) | Harold Hotelling |
| Type≠ | Robust multivariate association | Multivariate linear dimension reduction and association |
| Source fondatrice≠ | Croux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569. link ↗ | Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377. DOI ↗ |
| Alias | Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation | CCA, canonical variate analysis, canonical analysis, multiple canonical correlation |
| Apparentées | 4 | 4 |
| Résumé≠ | 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. |
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