论文标题
用于建模高维空间极端的有效工作流程
An Efficient Workflow for Modelling High-Dimensional Spatial Extremes
论文作者
论文摘要
高维空间极端的成功模型原则上应该能够描述在增加的水平上弱化的极端依赖性，又可以描述最大依赖类的类型的变化，这是位置之间距离的函数。此外，该模型应允许使用有效从数据中提取信息的推理方法进行计算上的推理，这些方法可以鲁棒化，这些方法是符合指定性的。在本文中，我们演示了如何通过开发全面的方法学工作流程来满足所有这些要求，以使用空间条件极端模型在同时对R-Inla进行快速推断，以有效地使用空间条件极端模型对高维空间极端模型进行有效的贝叶斯建模。然后，我们提出了一种事后调整方法，该方法通过正确考虑可能的模型错误指定，从而导致更强的推断。开发的方法用于对挪威高分辨率雷达数据的极端小时沉淀进行建模。推论在计算上是有效的，结果模型拟合成功地捕获了数据的极端依赖性结构的主要趋势。通过调整可能的错误指定来鲁棒化模型拟合进一步改善模型性能。
A successful model for high-dimensional spatial extremes should, in principle, be able to describe both weakening extremal dependence at increasing levels and changes in the type of extremal dependence class as a function of the distance between locations. Furthermore, the model should allow for computationally tractable inference using inference methods that efficiently extract information from data and that are robust to model misspecification. In this paper, we demonstrate how to fulfil all these requirements by developing a comprehensive methodological workflow for efficient Bayesian modelling of high-dimensional spatial extremes using the spatial conditional extremes model while performing fast inference with R-INLA. We then propose a post hoc adjustment method that results in more robust inference by properly accounting for possible model misspecification. The developed methodology is applied for modelling extreme hourly precipitation from high-resolution radar data in Norway. Inference is computationally efficient, and the resulting model fit successfully captures the main trends in the extremal dependence structure of the data. Robustifying the model fit by adjusting for possible misspecification further improves model performance.
