In extreme value analysis, sensitivity of inference to the definition of extreme event is a paramount issue. Under the peaks-over-threshold approach, this translates directly into the need of fitting a Generalized Pareto distribution to observations above a suitable level that balances bias versus variance of estimates. Selection methodologies established in the literature face recurrent challenges such as an inherent subjectivity or high computational intensity. We suggest a truly automated method for threshold detection, aiming at time efficiency and elimination of subjective judgment. Based on the well-established theory of L-moments, this versatile data-driven technique can handle batch processing of large collections of extremes data, while also presenting good performance on small samples. The technique’s performance is evaluated in a large simulation study and illustrated with significant wave height data sets from the literature. We find that it compares favorably to other state-of-the-art methods regarding the choice of threshold, associated parameter estimation and the ultimate goal of computationally efficient return level estimation.

在极值分析中，对极端事件定义的推理敏感性是最重要的问题。在“阈值之上的峰值”方法下，这直接转化为需要将广义Pareto分布拟合到合适水平以上的观测值，以平衡估计偏差与估计方差。文献中建立的选择方法面临反复出现的挑战，例如固有的主观性或较高的计算强度。我们建议一种真正自动化的阈值检测方法，旨在提高时间效率并消除主观判断。基于公认的L矩理论，这种通用的数据驱动技术可以处理大量极端数据集合的批处理，同时在小样本上也具有良好的性能。在一项大型模拟研究中对该技术的性能进行了评估，并通过文献中的重要波高数据集进行了说明。我们发现，在阈值的选择，相关参数估计以及计算效率高的收益水平估计的最终目标方面，它与其他最新方法相比具有优势。