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| 稳健典型相关分析 (Robust CCA)× | 典型相关分析× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 2003 | 1936 |
| 提出者≠ | Croux & Dehon (building on Hotelling's CCA framework) | Harold Hotelling |
| 类型≠ | Robust multivariate association | Multivariate linear dimension reduction and association |
| 开创性文献≠ | 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 ↗ |
| 别名 | Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation | CCA, canonical variate analysis, canonical analysis, multiple canonical correlation |
| 相关 | 4 | 4 |
| 摘要≠ | 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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