极大稳定过程的似然推理
Likelihood-based inference for max-stable processes
摘要：
在过去的十年中，最大稳定过程已经成为空间极值统计建模的常用工具。然而，由于多元密度函数的不可用，它们的应用是复杂的，因此基于似然的方法仍然远远不能提供一个完整和灵活的推理框架。在这篇文章中，我们开发了非常实用的，基于似然的方法来拟合最大稳定的过程来自一个复合似然方法。该程序是足够可靠和通用的，允许同时建模的边际和依赖参数在空间背景下，在一个温和的计算成本。这种方法的效用是通过模拟检查，并说明了美国降水极端的分析。
英文摘要：
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.
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