论文标题
在高维度中的空间极端建模的分布推断
Distributed Inference for Spatial Extremes Modeling in High Dimensions
论文作者
论文摘要
极端环境事件经常表现出空间和时间依赖性。这些数据通常是使用最大稳定过程（MSP）建模的。 MSP在计算上是过时的，可容纳十二个观测值，并采用假定的计算效率方法，例如复合可能性剩余的计算繁重，并有几百个观察值。在本文中，我们提出了一种基于空间域子集的局部建模的空间分区方法，该模型在计算和统计上有效的推论提供了一种空间分区。 MSP的边际和依赖性参数是在使用审查的成对复合可能性的观测值上局部估计的，并使用修改的矩矩程序合并。提出的分布式方法扩展到估计空间变化的系数模型，以提供边缘参数中空间变化的计算有效建模。我们证明了估计量的一致性和渐近正态性，并从经验上表明，我们的方法导致参数估计值的偏差令人惊讶地减少了完整的数据方法。我们通过模拟以及对美国地质调查局的流量数据进行分析来说明我们方法的灵活性和实用性。
Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using max stable processes (MSPs). MSPs are computationally prohibitive to fit for as few as a dozen observations, with supposed computationally-efficient approaches like the composite likelihood remaining computationally burdensome with a few hundred observations. In this paper, we propose a spatial partitioning approach based on local modeling of subsets of the spatial domain that delivers computationally and statistically efficient inference. Marginal and dependence parameters of the MSP are estimated locally on subsets of observations using censored pairwise composite likelihood, and combined using a modified generalized method of moments procedure. The proposed distributed approach is extended to estimate spatially varying coefficient models to deliver computationally efficient modeling of spatial variation in marginal parameters. We demonstrate consistency and asymptotic normality of estimators, and show empirically that our approach leads to a surprising reduction in bias of parameter estimates over a full data approach. We illustrate the flexibility and practicability of our approach through simulations and the analysis of streamflow data from the U.S. Geological Survey.
