Hierarchical Transformed Scale Mixtures for Flexible Modeling of Spatial Extremes on Datasets With Many Locations
面向多地点空间极值的层级变换尺度混合模型AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究空间环境极值与火灾威胁，尾部依赖这一术语本身不足以归入金融尾部风险。
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面向多地点空间极值的层级变换尺度混合模型
- 研究问题
- 如何在保留不同尾部依赖类型之间转换能力的同时，降低多地点空间极值推断的计算负担，并刻画火灾威胁指标的联合尾部？
- 方法与思路
- 作者修改高斯过程尺度混合模型，构建避免多元高斯分布函数计算的贝叶斯层级模型，以阈值超越数据及并行 MCMC 拟合。
- 材料陈述的贡献
- 摘要称修改保留原模型的极值依赖性质并简化计算；美国大平原应用发现随程度衰减的尾部依赖，极限极值模型无法捕捉。
- 阅读关注点
- 应核对模型修改与尾部性质保留的条件、阈值选择、覆盖分析和跨模型检查，以及多地点计算规模与应用结论的证据。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；具体依赖形式、计算收益与验证数值未提供，环境极值结果不能直接推广到金融风险。
原文摘要
–Flexible spatial models that allow transitions between tail dependence classes have recently appeared in the literature. However, inference for these models is computationally prohibitive, even in moderate dimensions, due to the necessity of repeatedly evaluating the multivariate Gaussian distribution function. In this work, we attempt to achieve truly high-dimensional inference for extremes of spatial processes, while retaining the desirable flexibility in the tail dependence structure, by modifying an established class of models based on scale mixtures Gaussian processes. We show that the desired extremal dependence properties from the original models are preserved under the modification, and demonstrate that the corresponding Bayesian hierarchical model does not involve the expensive computation of the multivariate Gaussian distribution function. We fit our model to exceedances of a high threshold, and perform coverage analyses and cross-model checks to validate its ability to capture different types of tail characteristics. We use a standard adaptive Metropolis algorithm for model fitting, and further accelerate the computation via parallelization and Rcpp. Lastly, we apply the model to a dataset of a fire threat index on the Great Plains region of the United States, which is vulnerable to massively destructive wildfires. We find that the joint tail of the fire threat index exhibits a decaying dependence structure that cannot be captured by limiting extreme value models. Supplementary materials for this article are available online.