An introduction to recent advances in high/infinite dimensional statistics - ScienceDirect Skip
to main contentSkip to article Elsevier logo Journals & Books Search RegisterSign in View …

We use tail expectiles to estimate alternative measures to the value at risk and marginal
expected shortfall, which are two instruments of risk protection of utmost importance in …

Extreme quantile regression provides estimates of conditional quantiles outside the range of
the data. Classical quantile regression performs poorly in such cases since data in the tail …

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 …

Convex quantile regression (CQR) is a fully nonparametric approach to estimating quantile
functions, which has proved useful in many applications of productivity and efficiency …

Extreme M-quantiles as risk measures: From L1 to Lp optimization Page 1 Bernoulli 25(1), 2019,
264–309 https://doi.org/10.3150/17-BEJ987 Extreme M-quantiles as risk measures: From L 1 to L …

Estimating software effort has been a largely unsolved problem for decades. One of the main
reasons that hinders building accurate estimation models is the often heterogeneous nature …

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 …

We present families of nonparametric estimators for the conditional tail index of a Pareto-
type distribution in the presence of random covariates. These families are constructed from …

For heavy-tailed distributions, the so-called tail index is an important parameter that controls
the behavior of the tail distribution and is thus of primary interest to estimate extreme …