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On the approximation of extreme quantiles with neural networks

Abstract : In this study, we propose a new parametrization for the generator of a Generative adversarial network (GAN) adapted to data from heavy-tailed distributions. We provide an analysis of the uniform error between an extreme quantile and its GAN approximation. Numerical experiments are conducted both on real and simulated data.
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https://hal.inria.fr/hal-03268702
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Submitted on : Wednesday, June 23, 2021 - 1:54:42 PM
Last modification on : Monday, February 21, 2022 - 8:08:02 AM
Long-term archiving on: : Friday, September 24, 2021 - 6:41:34 PM

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Michaël Allouche, Stéphane Girard, Emmanuel Gobet. On the approximation of extreme quantiles with neural networks. SFdS 2021 - 52èmes Journées de Statistique de la Société Française de Statistique, Jun 2021, Nice, France. pp.1-5. ⟨hal-03268702⟩

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