Likelihood-based inference for max-stable processes
Abstract
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.
Cite
@article{arxiv.0902.3060,
title = {Likelihood-based inference for max-stable processes},
author = {Simone A. Padoan and Mathieu Ribatet and Scott A. Sisson},
journal= {arXiv preprint arXiv:0902.3060},
year = {2009}
}