Titre :

Tail and quantile estimation for real-valued β-mixing spatial data

Auteur(s) :

Tchazino, Tchamiè [Auteur]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Diop, Aliou [Auteur]

Mot(s)-clé(s) en anglais :

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

Discipline(s) HAL :

Statistiques [stat]
Mathématiques [math]

Résumé en anglais : [en]

This paper deals with extreme-value index estimation of a heavy-tailed distribution of a spatial dependent process. We are particularlyinterested in spatial rare events of a β−mixing process. Given a sta-tionary real-valued ...
Lire la suite >This paper deals with extreme-value index estimation of a heavy-tailed distribution of a spatial dependent process. We are particularlyinterested in spatial rare events of a β−mixing process. Given a sta-tionary real-valued multidimensional spatial process {X_i,i ∈ Z^N}, weinvestigate its heavy-tail index estimation. Asymptotic properties ofthe corresponding estimator are established under mildmixingcondi-tions. The particularity of the tail proposed estimator is based on thespatial nature of the sample and its unbiased and reduced variance prop-erties compared to well known tail index estimators. Extreme quantileestimation is also deduced. A numerical study on synthetic and realdatasets is conducted to assess the finite-sample behaviour of the pro-posed estimators.Lire moins >

Langue :

Anglais

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL