| Summary: | Copulas have become a useful tool for modeling data when the dependence among random variables exists and the multivariate normality assumption is not fulfilled. The copulas have been applied in several fields. In finance, copulas are used in asset modeling and risk management. In biomedical studies, copulas are used to model correlated lifetimes and competitive risks [1]. In engineering, copulas are used in multivariate process control and hydrological modeling [2]. The interest in modeling multivariate problems involving dependent variables is generalized in several areas, making this methodology in a convenient way to model the dependence structure of random variables. However, in practice there is not a standard method for selecting a copula among several possible models, so that the choice of an appropriate copula is one of the greatest challenges facing the researcher. In this paper some graphical methods for detecting dependencies among random variables are discussed.
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