Extreme Values in Finance, Telecommunications, and the Environment

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Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (2018, methodology): “The constraints in terms of URLLC are characterized using extreme value theory and modeled as the tail distribution of the network-wide queue lengths over a predefined threshold.”
Statistical Modelling of Spatial Extremes (2012, background): “ther reviews are provided by Kotz and Nadarajah (2000) and Finkenstädt and Rootzén (2004). A key issue in applications is that inferences may be required well beyond the observed tail of the data, and so an assumption of stability is required: mathematical regularities in the unobservable tail of th”
Copulas: Tales and facts (2006, background): “Unfortunately, real-life data often turn out to be highly non-Gaussian, see for example Chapter 6 in Embrechts et al. [5] or Mikosch [18] for some evidence of Pareto like tails for returns.”
Statistical Modeling of Spatial Extremes (2012, background): “Further reviews are provided by Kotz and Nadarajah (2000) and Finkenstädt and Rootzén (2004).”
Likelihood estimators for multivariate extremes (2014, background): “In the multivariate framework, the notion of exceeding a given threshold may be interpreted in various ways, thereby yielding different threshold-based estimators (see, e.g., Coles and Tawn 1991; Resnick 1987; Beirlant et al. 2004; Fougères 2004).”