Details

Autor(en) / Beteiligte

• Padoan, S. A.
• Ribatet, M.
• Sisson, S. A.

Titel

Likelihood-Based Inference for Max-Stable Processes

Ist Teil von

• Journal of the American Statistical Association, 2010-03, Vol.105 (489), p.263-277

Ort / Verlag

Alexandria: Taylor & Francis

Erscheinungsjahr

2010

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 United States precipitation extremes.