Title :

Conditional tail and quantile estimation for real-valued β-mixing spatial data

Author(s) :

Tchazino, Tchamiè [Auteur]
Université d’Abomey-Calavi = University of Abomey Calavi [UAC]
Diop, Aliou [Auteur]
Université Gaston Berger - Laboratoire d’études et de recherches en statistiques et développement [Sénégal] [LERSTAD]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]

English keyword(s) :

Asymptotic normality
β-mixing
conditional extreme value index
bias correction
spatial dependence
functional estimation

HAL domain(s) :

Statistiques [stat]

English abstract : [en]

This paper deals with the estimation of the tail index of a conditionnal heavy-tailed distribution of a spatial process. We are particularly interested in the estimation of conditionnal spatial rare events when the process ...
Show more >This paper deals with the estimation of the tail index of a conditionnal heavy-tailed distribution of a spatial process. We are particularly interested in the estimation of conditionnal spatial rare events when the process is β−mixing. Given a conditionnal stationary real-valued multidimensional spatial process Y x i , i ∈ Z N , we investigate its conditional heavy-tail index estimation and the corresponding conditional quantile. Asymptotic properties of the corresponding estimators are established under mild mixing conditions. The particularity of the tail proposed estimator is based on the spatial nature of the sample and its unbiased and reduced variance properties compared to well known conditional tail index estimators. A numerical study on synthetic and real datasets is conducted to assess the finite-sample behaviour of the proposed estimators.Show less >

Language :

Anglais

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL