On the estimation of extreme quantiles with neural networks - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

On the estimation of extreme quantiles with neural networks


We propose new parametrizations for neural networks in order to estimate extreme quantiles in both non-conditional and conditional heavy-tailed settings. All proposed neural network estimators feature a bias correction based on an extension of the usual second-order condition to an arbitrary order. The convergence rate of the uniform error between extreme log-quantiles and their neural network approximation is established. The finite sample performances of the non-conditional neural network estimator are compared to other bias-reduced extreme-value competitors on simulated data. It is shown that our method outperforms them in difficult heavy-tailed situations where other estimators almost all fail. The source code is available at https://github.com/michael-allouche/nn-quantile-extrapolation.git. Finally, the conditional neural network estimators are implemented to investigate the behaviour of extreme rainfalls as functions of their geographical location in the southern part of France.
No file

Dates and versions

hal-04124085 , version 1 (09-06-2023)


  • HAL Id : hal-04124085 , version 1


Michaël Allouche, Stéphane Girard, Emmanuel Gobet. On the estimation of extreme quantiles with neural networks. Journée "Evénements extrêmes et risques", réseau RESSTE (Risques, Extrêmes et Statistique Spatio-TEmporelle), Jun 2023, Marseille, France. ⟨hal-04124085⟩
84 View
0 Download


Gmail Mastodon Facebook X LinkedIn More