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
