Statistical issues arising in modelling univariate extremes of a random sample have been
successfully used in the most diverse fields, such as biometrics, finance, insurance and risk …

This review paper focuses on statistical issues arising in modeling univariate extremes of a
random sample. In the last three decades there has been a shift from the area of parametric …

The classical Hill estimator of a positive extreme value index (EVI) can be regarded as the
logarithm of the geometric mean, or equivalently the logarithm of the mean of order p= 0, of a …

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 …

In the paper, we propose a new idea in the tail-index estimation. This idea allows us to
improve the asymptotic performance of the classical Hill estimator and other most popular …

In this article, we are interested in the comparison, under a third-order framework, of classes
of second-order, reduced-bias tail index estimators, giving particular emphasis to minimum …

Heavy-tailed distributions are often encountered in economics, finance, biology,
telecommunications, geology, etc. The heaviness of a tail is measured by a tail index …

In the field of Statistics of Extremes, the most common assumption on any set of univariate
data is to consider them as a complete sample of either independent and identically …

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 …

Lehmer's mean-of-order p (L p) generalizes the arithmetic mean, and L p extreme value
index (EVI)-estimators can be easily built, as a generalization of the classical Hill EVI …