Extreme Value Statistics for Analysing Simulated Environmental Extremes (2312.13725v1)

Abstract: We present the methods employed by team Uniofbathtopia' as part of the Data Challenge organised for the 13th International Conference on Extreme Value Analysis (EVA2023), including our winning entry for the third sub-challenge. Our approaches unite ideas from extreme value theory, which provides a statistical framework for the estimation of probabilities/return levels associated with rare events, with techniques from unsupervised statistical learning, such as clustering and support identification. The methods are demonstrated on the data provided for the Data Challenge -- environmental data sampled from the fantasy country ofUtopia' -- but the underlying assumptions and frameworks should apply in more general settings and applications.