Add to ORKGIf one applies the Hill, Pickands or Dekkers-Einmahl-de Haan estimators of the tail index of a distribution to data which are rounded off one often observes that these estimators oscillate strongly as a function of the number k of order statistics involved. We study this phenomenon in the case of a Pareto distribution. We provide formulas for the expected value and variance of the Hill estimator and give bounds on k when the central limit theorem is still applicable. We illustrate the theory by using simulated and real-life data
textabstractThe selection of upper order statistics in tail estimation is notoriously difficult. Mos...
Let Z1, Z2,... be i.i.d. random variables with tail behaviour P (Z1> z) = r(z)e−Rz, where r is a ...
In extreme value statistics, the extreme value index is a well-known parameter to measure the tail h...
Estimation of the Pareto tail index from extreme order statistics is an important problem in many se...
This thesis focuses on the analysis of heavy-tailed distributions, which are widely applied to model...
In this work we discuss tail index estimation for heavy-tailed distributions with an emphasis on rob...
In this paper we analyze the asymptotic properties of the popular distribution tail index estimator ...
We introduce a robust and asymptotically unbiased estimator for the tail index of Pareto-type distri...
This paper presents an adaptive version of the Hill estimator based on Lespki's model selection meth...
We present asymptotic expansions for two well-known estimators of the tail index of a distribution-t...
Successful estimation of the Pareto tail index from extreme order statistics relies heavily on the p...
The problem of estimating the tail index in heavy-tailed distributions is very important in a variet...
Z. Fabian and M. Stehlik (2009) investigate a new estimator of extreme value index of a distribution...
AbstractWe present a nonparametric family of estimators for the tail index of a Pareto-type distribu...
In this thesis we consider estimation of the tail index for heavy tailed stochastic volatility model...
textabstractThe selection of upper order statistics in tail estimation is notoriously difficult. Mos...
Let Z1, Z2,... be i.i.d. random variables with tail behaviour P (Z1> z) = r(z)e−Rz, where r is a ...
In extreme value statistics, the extreme value index is a well-known parameter to measure the tail h...
Estimation of the Pareto tail index from extreme order statistics is an important problem in many se...
This thesis focuses on the analysis of heavy-tailed distributions, which are widely applied to model...
In this work we discuss tail index estimation for heavy-tailed distributions with an emphasis on rob...
In this paper we analyze the asymptotic properties of the popular distribution tail index estimator ...
We introduce a robust and asymptotically unbiased estimator for the tail index of Pareto-type distri...
This paper presents an adaptive version of the Hill estimator based on Lespki's model selection meth...
We present asymptotic expansions for two well-known estimators of the tail index of a distribution-t...
Successful estimation of the Pareto tail index from extreme order statistics relies heavily on the p...
The problem of estimating the tail index in heavy-tailed distributions is very important in a variet...
Z. Fabian and M. Stehlik (2009) investigate a new estimator of extreme value index of a distribution...
AbstractWe present a nonparametric family of estimators for the tail index of a Pareto-type distribu...
In this thesis we consider estimation of the tail index for heavy tailed stochastic volatility model...
textabstractThe selection of upper order statistics in tail estimation is notoriously difficult. Mos...
Let Z1, Z2,... be i.i.d. random variables with tail behaviour P (Z1> z) = r(z)e−Rz, where r is a ...
In extreme value statistics, the extreme value index is a well-known parameter to measure the tail h...