Construction et estimation de copules en grande dimension

Abstract : In the last decades, copulas have been more and more used in statistical modeling. Their popularity owes much to the fact that they allow to separate the analysis of the margins from the analysis of the dependence structure induced by the underlying distribution. This renders easier the modeling of non Gaussian distributions, and, in particular, it allows to take into account non linear dependencies between random variables. Finance and hydrology are two examples of scientific fields where the use of copulas is nowadays standard. However, while many bivariate families exist in the literature, multivariate/high dimensional copulas are much more difficult to construct. This thesis presents three contributions to copula modeling and inference, with an emphasis on high dimensional problems. The first model writes as a product of bivariate copulas and is underlain by a tree structure where each edge represents a bivariate copula. Hence, we are able to model different pairs with different dependence properties. The second one is a factor model built on a nonparametric class of bivariate copulas. It exhibits a good balance between tractability and flexibility. This thesis also deals with the parametric inference of copula models in general. Indeed, the asymptotic properties of a weighted least-squares estimator based on dependence coefficients are established. The models and methods have been applied to hydrological data (flow rates and rain falls).
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Gildas Mazo. Construction et estimation de copules en grande dimension. Autres [stat.ML]. Université de Grenoble, 2014. Français. ⟨NNT : 2014GRENM058⟩. ⟨tel-01130963v2⟩

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