On Choice of Hyper-parameter in Extreme Value Theory based on Machine Learning Techniques
This work addresses a specific challenge in statistical analysis of extreme events, but it appears incremental as it applies existing machine learning techniques to EVT.
The paper tackles the problem of selecting hyper-parameters in extreme value theory (EVT) by proposing a new method based on machine learning techniques, and it demonstrates good usability through experiments on real-world data.
Extreme value theory (EVT) is a statistical tool for analysis of extreme events. It has a strong theoretical background, however, we need to choose hyper-parameters to apply EVT. In recent studies of machine learning, techniques of choosing hyper-parameters have been well-studied. In this paper, we propose a new method of choosing hyper-parameters in EVT based on machine learning techniques. We also experiment our method to real-world data and show good usability of our method.