Reduced-bias versions of a very simple generalization of the 'classical'Hill estimator of a
positive extreme value index (EVI) are put forward. The Hill estimator can be regarded as the …

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 …

The Lehmer mean of order p of k positive numbers (a1,..., ak) is defined by∑ ki= 1 api/∑ ki=
1 ap− 1 i, generalizing both the arithmetic mean (p= 1) and the harmonic mean (p= 0). Given …

A recent class of estimators of a positive extreme value index (EVI), related to a mean-of-
order-p (MOP) class of EVI-estimators is enlarged and studied for finite samples through a …

Weissman extrapolation methodology for estimating extreme quantiles from heavy-tailed
distributions is based on two estimators: an order statistic to estimate an intermediate …

In finance, insurance and statistical quality control, among many other areas of appli [1]
cation, a typical requirement is to estimate the value-at-risk (VaR) at a small level q, ie. a …

Most of the estimators of parameters of rare and large events, among which we distinguish
the extreme value index (EVI) for maxima, one of the primary parameters in statistical …

The 'School of Extremes' in Portugal is nowadays well recognized by the international
scientific community. This recognition is mainly due to the scientific work of Tiago de Oliveira …

On the basis of a sample of either independent, identically distributed or possibly weakly
dependent and stationary random variables from an unknown model F with a heavy right-tail …

The value-at-risk (VaR) at a small level q, 0< q< 1, is the size of the loss that occurs with a
probability q. Semi-parametric partially reduced-bias (PRB) VaR-estimation procedures …