Papers

Likelihood-based inference for max-stable processes
Padoan S. A, M. Ribatet and S. A. Sisson (in press). JASA, in press.
[Preprint]

Abstract: The last decade has seen max-stable processes emerge as a common tool for the statistical modelling 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 modelling 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.

Keywords: Composite likelihood; Extreme value theory; Max-stable processes; Pseudo-likelihood, Rainfall; Spatial Extremes.