High-dimensional inference for extreme value indices
这项研究提出了高维情形下比较极值指数的新检验方法，适用于弱和一般尾部依赖场景，模拟显示优于现有方法，并通过两个数据集展示了实际应用。
When applying multivariate extreme value statistics to analyze tail risk in compound events defined by a multivariate random vector, one often assumes that all dimensions share the same extreme value index. While such an assumption can be tested using a Wald-type test, the performance of such a test deteriorates as the dimensionality increases.This paper introduces novel tests for comparing extreme value indices in high-dimensional settings, under both weak and general cross-sectional tail dependence. We establish the asymptotic behavior of the proposed tests. The proposed tests significantly outperform existing methods in high-dimensional scenarios in simulations. We demonstrate real-life applications of the proposed tests for two datasets previously assumed to have identical extreme value indices across all dimensions.