Likelihood-based inference for max-stable processes
| dc.creator | Padoan, Simone A. | |
| dc.creator | Ribatet, Mathieu | |
| dc.creator | Sisson, Scott A. | |
| dc.date | 2009-02-18 | |
| dc.date | 2009-02-23 | |
| dc.date.accessioned | 2026-07-07T12:44:54Z | |
| dc.date.available | 2026-07-07T12:44:54Z | |
| dc.description | 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. | |
| dc.identifier | https://arxiv.org/abs/0902.3060 | |
| dc.identifier | http://arxiv.org/abs/0902.3060 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/220932 | |
| dc.subject | Methodology | |
| dc.title | Likelihood-based inference for max-stable processes | |
| dc.type | text |