Estimation of extreme quantiles from heavy and light tailed distributions

Estimating extreme quantiles of Weibull tail- distributions. Comparison of Weibull

In this chapter we propose inference (i.e. estimation and testing) for vast dimensional elliptical distributions. Estimation is based on quantiles, which always exist regardless of

Bias-reduced extreme quantiles estimators of Weibull tail-distributions. A new estimation method for Weibull-type tails based on the mean

However, the results on the extreme value index, in this case, are stated with a restrictive assumption on the ultimate probability of non-censoring in the tail and there is no

Abstract This paper presents new approaches for the estimation of the ex- treme value index in the framework of randomly censored (from the right) samples, based on the ideas

2 we recall the known results for the sample covariance matrix estimator under light-tailed assumptions, together with some recent high-probability results for an alternative

The quantile function at an arbitrarily high extreme level can then be consistently deduced from its value at a typically much smaller level provided γ can be consistently

In this communication, we first build simple extreme analogues of Wang distortion risk measures and we show how this makes it possible to consider many standard measures of