Simultaneous autoregressive models for spatial extremes
Simultaneous autoregressive models for spatial extremes
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DOI：
10.1002/env.2656
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发表时间：
2020-09-16
期刊：
影响因子：
1.7
通讯作者：
Thibaud, Emeric
中科院分区：
文献类型：
--
作者：
Fix, Miranda J.;Cooley, Daniel S.;Thibaud, Emeric
受大气科学中大网格数据集的广泛使用的启发，我们提出了一个新的模型，面积数据的极端的启发，在经典的空间统计的同步自回归（SAR）模型。我们的极端SAR模型扩展了最近的工作，变换线性运算适用于经常变化的随机向量，是唯一的极端模型直接类似于一个经典的线性模型。另一个吸引力是它的简单性;给定邻近矩阵W，空间依赖性由单个参数ρ描述。我们开发了一种估计方法，最大限度地减少尾部成对依赖矩阵（TPDM）的拟合模型和估计TPDM之间的差异。将该方法应用于模拟数据表明，即使在模型误指定的情况下，它也能够产生良好的极值空间依赖估计，并且还产生合理的不确定性估计。我们还将该方法应用于网格降水观测研究区域东北部科罗拉多，并发现，一个单参数极端SAR模型配对的邻域结构，占较长的范围依赖有效地模型空间依赖这些数据。
Motivated by the widespread use of large gridded data sets in the atmospheric sciences, we propose a new model for extremes of areal data that is inspired by the simultaneous autoregressive (SAR) model in classical spatial statistics. Our extreme SAR model extends recent work on transformed-linear operations applied to regularly varying random vectors, and is unique among extremes models in being directly analogous to a classical linear model. An additional appeal is its simplicity; given a proximity matrixW, spatial dependence is described by a single parameter rho. We develop an estimation method that minimizes the discrepancy between the tail pairwise dependence matrix (TPDM) for the fitted model and the estimated TPDM. Applying this method to simulated data demonstrates that it is able to produce good estimates of extremal spatial dependence even in the case of model misspecification, and additionally produces reasonable estimates of uncertainty. We also apply the method to gridded precipitation observations for a study region over northeast Colorado, and find that a single-parameter extreme SAR model paired with a neighborhood structure which accounts for longer range dependence effectively models spatial dependence in these data.