论文标题
改善了单变量极端的风险措施的推断
Improved inference on risk measures for univariate extremes
论文作者
论文摘要
我们讨论了可能渐近性的可能性渐近性问题的使用，重点是对高分位数的估计以及类似的不确定性量化风险摘要。我们研究基于切线指数模型的高阶近似是否可以提供改进的推论，并得出结论，基于最大值的推理通常对轻度模型不指定且基于特征的置信区间的置信区间通常是足够的，而基于阈值超过阈值的推理通常会很偏见，但可以通过较高的方法进行改进，至少可以改善较高的方法。我们使用这些方法来阐明委内瑞拉的灾难性降雨，威尼斯的洪水以及意大利半居民的一生。
We discuss the use of likelihood asymptotics for inference on risk measures in univariate extreme value problems, focusing on estimation of high quantiles and similar summaries of risk for uncertainty quantification. We study whether higher-order approximation based on the tangent exponential model can provide improved inferences, and conclude that inference based on maxima is generally robust to mild model misspecification and that profile likelihood-based confidence intervals will often be adequate, whereas inferences based on threshold exceedances can be badly biased but may be improved by higher-order methods, at least for moderate sample sizes. We use the methods to shed light on catastrophic rainfall in Venezuela, flooding in Venice, and the lifetimes of Italian semi-supercentenarians.
