Nonparametric functional data analysis is a field whose development started some 15 years
ago and there is a very extensive literature on the topic (hundreds of papers published now) …

In this paper, we introduce a new risk measure, the so‐called conditional tail moment. It is
defined as the moment of order a≥ 0 of the loss distribution above the upper α‐quantile …

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 …

Feedforward neural networks based on Rectified linear units (ReLU) cannot efficiently
approximate quantile functions which are not bounded, especially in the case of heavy …

In extreme value theory, the extreme-value index is a parameter that controls the behavior of
a cumulative distribution function in its right tail. Estimating this parameter is thus the first …

Considering extreme quantiles is a popular way to understand the tail of a distribution. While
they have been extensively studied for univariate distributions, much less has been done for …

The present article is devoted to the semi-parametric estimation of multivariate expectiles for
extreme levels. The considered multivariate risk measures also include the possible …

In this article, we propose a test for the multivariate regular variation (MRV) model. The test
is based on testing whether the extreme value indices of the radial component conditional …

ABSTRACT A quantile autoregresive model is a useful extension of classical autoregresive
models as it can capture the influences of conditioning variables on the location, scale, and …

In a regression context where a response variable Y∈ ℝ is recorded with a covariate X∈ ℝ
p, two situations can occur simultaneously:(a) we are interested in the tail of the conditional …