Aboubacrène Ag Ahmad

University of Sciences, Technics and Technology of Bamako · Mathematics and computer science

We consider a location-dispersion regression model for heavy-tailed distributions when the multidimensional covariate is deterministic. In a first step, nonparametric estimators of the regression and dispersion functions are introduced. This permits, in a second step, to derive an estimator of the conditional extreme-value index computed on the res...

The modeling of extreme events arises in many fields such as finance, insurance or environmental science. A recurrent statistical problem is then the estimation of extreme quantiles associated with a random variable $Y$ recorded simultaneously with a multidimensional covariate $x\in{\mathbb R}^d$, the goal being to describe how tail characteristic...

L'objectif principal de cette thèse est de proposer de nouveaux estimateurs de l’indice des valeurs extrêmes (indice de queue) ainsi que des quantiles extrêmes conditionnels pour une famille de distributions à queue lourde. La famille de distributions considérée est définie à partir d’un modèle de régression avec des paramètres fonctionnels de posi...

We consider a location-dispersion regression model for heavy-tailed distributions when the multidimensional covariate is deterministic. In a first step, nonparametric estimators of the regression and dispersion functions are introduced. This permits, in a second step, to derive an estimator of the conditional extreme-value index computed on the res...

We introduce a location-scale model for conditional heavy-tailed distributions when the covariate is deterministic. First, nonparametric estimators of the location and scale functions are introduced. Second, an estimator of the conditional extreme-value index is derived. The asymptotic properties of the estimators are established under mild assumpt...