Compară metode

	Modele de copulă (Gaussian, t, Clayton, Gumbel, Frank)	Coeficientul de corelație moment-produs Pearson (r)
Domeniu	Finanțe	Statistică
Familie	Regression model	Hypothesis test
Anul apariției	1959	1895
Autorul original	Sklar (1959); dependence-concept treatment by Joe (1997)	Karl Pearson
Tip	Dependence model	Parametric correlation
Sursa seminală	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.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
Denumiri alternative	copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank)	pearson r, product-moment correlation, bivariate correlation, Pearson Korelasyon Analizi
Înrudite	5	4
Rezumat	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.