Estimation method for mixture copula models in hydrological context

Hydrological extreme events are characterized by several correlated variables. For a better associated risk assessment, the dependence structure between these variables must be taken into account by considering copulas. On the other hand, extreme events are generated from different phenomena. In such cases, the margins and/ or copula may be affected. Hence, mixture copula should be considered. Recently, there have been an increasing number of studies dealing with the parameter estimation of mixture copula. However, existing methods have several drawbacks. To overcome these drawbacks, we propose a new parameter estimation approach for the mixture copula models, based on the maximum pseudo-likelihood using a metaheuristic algorithm. A simulation study is conducted to evaluate the performance of the proposed method and to compare it with those of the widely used existing method. Results indicate that the proposed method estimates more accurately the parameters even with small sample sizes compared to the existing ones. An application to a real data set is also provided and validated with the available data.

Automated summary

Hydrological extreme events are composed of several correlated variables, and the dependence structure between these variables needs to be considered for better risk assessment using copulas. Mixture copula is suitable for extreme events generated from different phenomena, but existing parameter estimation methods for mixture copula have drawbacks. To overcome these drawbacks, a new parameter estimation approach based on the maximum pseudo-likelihood using a metaheuristic algorithm is proposed. Simulation and real data studies show that the proposed method can estimate parameters accurately even with small sample sizes compared to existing methods.