论文标题
空间数据的分层贝叶斯非反应性极值模型
A hierarchical Bayesian non-asymptotic extreme value model for spatial data
论文作者
论文摘要
极端降水的空间图对于防洪至关重要。为了产生降水回报水平的地图，我们提出了一种新颖的方法，以模拟空间分布的时间序列的集合，在该时间序列中，渐近假设是传统极端价值理论的典型代表。我们引入了一个贝叶斯分层模型，该模型解释了事件大小和事件分布的可能基本变化，这些变化是通过潜在的时间和空间过程描述的。空间依赖性的特征在于地理协变量，其效应未完全描述，协变量是由层次结构中的空间结构捕获的。通过模拟研究和对北卡罗来纳州北卡罗来纳州（美国）每日极端降雨的应用来说明该方法的性能。结果表明，我们大大降低了对最新技术状态的估计不确定性。
Spatial maps of extreme precipitation are crucial in flood protection. With the aim of producing maps of precipitation return levels, we propose a novel approach to model a collection of spatially distributed time series where the asymptotic assumption, typical of the traditional extreme value theory, is relaxed. We introduce a Bayesian hierarchical model that accounts for the possible underlying variability in the distribution of event magnitudes and occurrences, which are described through latent temporal and spatial processes. Spatial dependence is characterized by geographical covariates and effects not fully described by the covariates are captured by spatial structure in the hierarchies. The performance of the approach is illustrated through simulation studies and an application to daily rainfall extremes across North Carolina (USA). The results show that we significantly reduce the estimation uncertainty with respect to state of the art techniques.
