Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèles de copules (Gaussienne, t, Clayton, Gumbel, Frank)× | Corrélation de Pearson par le produit des moments× | |
|---|---|---|
| Domaine≠ | Finance | Statistique |
| Famille≠ | Regression model | Hypothesis test |
| Année d'origine≠ | 1959 | 1895 |
| Auteur d'origine≠ | Sklar (1959); dependence-concept treatment by Joe (1997) | Karl Pearson |
| Type≠ | Dependence model | Parametric correlation |
| Source fondatrice≠ | Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut Statistique de l'Université de Paris, 8, 229-231. link ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. DOI ↗ |
| Alias | copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank) | pearson r, product-moment correlation, bivariate correlation, Pearson Korelasyon Analizi |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | Copula models are a family of functions that describe the dependence structure between variables separately from their individual (marginal) distributions. The foundation is Sklar's theorem (1959), which shows that any multivariate distribution can be split into its marginals plus a copula; Joe (1997) developed the modern catalogue of dependence concepts. They are central to portfolio risk and credit modelling. | The Pearson product-moment correlation coefficient (r) is a parametric measure of the direction and strength of the linear association between two continuous variables. Introduced by Karl Pearson in 1895, it remains the most widely used bivariate correlation statistic in the social, health, and natural sciences. The coefficient ranges from −1 (perfect negative linear relationship) to +1 (perfect positive), with 0 indicating no linear association. |
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