Êtes-vous un étudiant de l'EPFL à la recherche d'un projet de semestre?
Travaillez avec nous sur des projets en science des données et en visualisation, et déployez votre projet sous forme d'application sur GraphSearch.
The last decade has seen max-stable processes emerge as a common tool for the statistical modeling of spatial extremes However, their application is complicated due to the unavailability of the multivariate density function and so likehhood-based methods remain far from providing a complete and flexible framework kit inference In this article we develop inferentially practical likehhood-based methods for fitting max-stable processes derived from a composite-likehhood approach The procedure is sufficiently reliable and versatile to permit the simultaneous modeling of marginal and dependence parameters in the spatial context at a moderate computational cost The utility of this methodology is examined via simulation. and illustrated by the analysts of United States precipitation extremes