发表机构
University of Massachusetts Amherst; Colorado State University（阿默斯特马萨诸塞大学; 科罗拉多州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对似然不可处理的多元极值模型，提出基于尾部成对依赖性的代理似然估计器，采用Hüsler-Reiss分布，在TLETS模型上优于现有方法，并应用于野火天气数据。
AI 中文摘要
许多多元极值模型的似然函数不可处理，迫使从业者使用替代拟合方法。尾部成对依赖性是对任何多元正则变化模型尾部依赖性的汇总度量。我们开发了一个用于模型拟合的目标函数，该函数依赖于尾部成对依赖性作为我们期望模型（没有似然函数）与代理模型（有似然函数）之间的纽带。我们采用二元Hüsler-Reiss分布作为代理模型，并证明了依赖性参数与尾部成对依赖性值之间存在一一对应关系。我们的代理似然估计器已针对Mhatre和Cooley（2024）的变换线性极值时间序列（TLETS）模型进行了全面开发，并应用于Wixson和Cooley（2023）的野火天气数据。模拟表明，代理似然是一种有竞争力的尾部成对依赖性估计器，在拟合TLETS模型方面优于现有方法，并且适用于基于似然的模型选择技术。当尾部依赖性较弱时，我们的估计器比现有估计器具有更小的偏差，减少了对偏差调整的需求。在没有这些调整的情况下，我们注意到过去和现在气候之间与天气相关的野火风险的尾部依赖性有所增加。
英文摘要
Many multivariate extremes models have intractable likelihoods requiring practitioners to use alternative fitting methods. The tail pairwise dependence is a summary measure of the dependence in the tail of any multivariate regular variation model. We develop an objective function for model fitting that relies on the tail pairwise dependence as the link between our desired model (that does not have a likelihood) and a proxy model (that has a likelihood). We employ the bivariate Hüsler-Reiss distribution as the proxy model and show that there is a one-to-one relationship between the dependence parameter and the tail pairwise dependence value. Our proxy-likelihood estimator is fully developed for the transformed linear extremes time series (TLETS) models of Mhatre and Cooley (2024) and is applied to the wildfire weather data of Wixson and Cooley (2023). Simulations demonstrate that the proxy-likelihood is a competitive TPD estimator, is better at fitting TLETS models than existing methods, and is amenable to likelihood-based model selection techniques. Our estimator has smaller bias when tail dependence is weak than existing estimators reducing the need for bias adjustments. Without these adjustments, we note an increase in the tail dependence in weather-related wildfire risk between past and present climates.
