Abstract
This chapter provides a brief summary of the latest results obtained in extreme value theory, and offers many suggestions for the reader interested in using these tools in a context where, in particular, statisticians and engineers cannot ignore the Big Data paradigm and the industrialization of machine learning tools, now essential components of modern artificial intelligence. Parallels are also made with other disciplines interested in extreme statistics.
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- 1.
That is, the spacing used between measurement times, which can limit or even prohibit the data being loaded into computer memory, and force the detection of extreme values to be conducted in situ.
- 2.
Especially, the sub-additivity of a risk measure \({\mathscr {R}}(X+X')\le {\mathscr {R}}(X)+{\mathscr {R}}(X')\). See [656] for more details.
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Andreewsky, M., Bernardara, P., Bousquet, N., Dutfoy, A., Parey, S. (2021). Perspectives. In: Bousquet, N., Bernardara, P. (eds) Extreme Value Theory with Applications to Natural Hazards. Springer, Cham. https://doi.org/10.1007/978-3-030-74942-2_12
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DOI: https://doi.org/10.1007/978-3-030-74942-2_12
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