Robust nonparametric estimation of the conditional tail dependence coefficient

Yuri Goegebeur (1) , Armelle Guillou (2) , Nguyen Khanh Le Ho (1) , Jing Qin (1)

1 IMADA - Department of Mathematics and Computer Science [Odense]
2 IRMA - Institut de Recherche Mathématique Avancée

We consider robust and nonparametric estimation of the coefficient of tail dependence in presence of random covariates. The estimator is obtained by fitting the extended Pareto distribution locally to properly transformed bivariate observations using the minimum density power divergence criterion. We establish convergence in probability and asymptotic normality of the proposed estimator under some regularity conditions. The finite sample performance is evaluated with a small simulation experiment, and the practical applicability of the method is illustrated on a real dataset of air pollution measurements.

Mots clés: Coefficient of tail dependence; robustness; local estimation; empirical process

Domaines: Statistiques [math.ST]

Yuri Goegebeur, Armelle Guillou, Nguyen Khanh Le Ho, Jing Qin. Robust nonparametric estimation of the conditional tail dependence coefficient. Journal of Multivariate Analysis, 2020, 178, ⟨10.1016/j.jmva.2020.104607⟩. ⟨hal-02269476⟩