Estimation of multivariate generalized gamma convolutions, application to dependence structure modelling in insurance.
Estimation de convolutions de lois Gamma généralisées multivariées; application à la modélisation des structures de dépendance en assurance.
Résumé
While modeling the dependence structure between several (re)insurance losses by an additive risk factor model, the infinite divisibility is a very desirable property. Unfortunately, if many useful distributions are infinitely divisible, computing the distributions of their pieces is usually a challenging task that requires heavy numerical computations. We propose an estimation algorithm for multivariate generalized gamma convolutions through Laguerre expansions. These distributions are divisible and usefull in dependence structure modeling.
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