Extremes in High Dimensions: Methods and Scalable Algorithms

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Publication date: 7 March 2023

Abstract: Extreme-value theory has been explored in considerable detail for univariate and low-dimensional observations, but the field is still in an early stage regarding high-dimensional multivariate observations. In this paper, we focus on H"usler-Reiss models and their domain of attraction, a popular class of models for multivariate extremes that exhibit some similarities to multivariate Gaussian distributions. We devise novel estimators for the parameters of this model based on score matching and equip these estimators with state-of-the-art theories for high-dimensional settings and with exceptionally scalable algorithms. We perform a simulation study to demonstrate that the estimators can estimate a large number of parameters reliably and fast; for example, we show that H"usler-Reiss models with thousands of parameters can be fitted within a couple of minutes on a standard laptop.

Has companion code repository: https://github.com/ledererlab/hdextremes

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