Likelihood-based inference for max-stable processes

Simone A. Padoan (1) , Mathieu Ribatet (2) , Scott Sisson (3)

1 EFLUM - Laboratory of Environmental Fluid Mechanics and Hydrology
2 Chair of Statistics
3 School of Mathematics and Statistics

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 likelihood-based methods remain far from providing a complete and flexible framework for inference. In this article we develop inferentially practical, likelihood-based methods for fitting max-stable processes derived from a composite-likelihood 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 analysis of U.S. precipitation extremes.

Domaines: Méthodologie [stat.ME]

Simone A. Padoan, Mathieu Ribatet, Scott Sisson. Likelihood-based inference for max-stable processes. 2008. ⟨hal-00361245v2⟩