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Asymptotic behaviour of the probability-weighted moments and penultimate approximation

Jean DieboltArmelle GuillouRym Worms — 2003

ESAIM: Probability and Statistics

The P.O.T. (Peaks-Over-Threshold) approach consists of using the Generalized Pareto Distribution (GPD) to approximate the distribution of excesses over a threshold. We use the probability-weighted moments to estimate the parameters of the approximating distribution. We study the asymptotic behaviour of these estimators (in particular their asymptotic bias) and also the functional bias of the GPD as an estimate of the distribution function of the excesses. We adapt penultimate approximation results...

Uniform strong consistency of a frontier estimator using kernel regression on high order moments

Stéphane GirardArmelle GuillouGilles Stupfler — 2014

ESAIM: Probability and Statistics

We consider the high order moments estimator of the frontier of a random pair, introduced by [S. Girard, A. Guillou and G. Stupfler, 116 (2013) 172–189]. In the present paper, we show that this estimator is strongly uniformly consistent on compact sets and its rate of convergence is given when the conditional cumulative distribution function belongs to the Hall class of distribution functions.

Asymptotic behaviour of the probability-weighted moments and penultimate approximation

Jean DieboltArmelle GuillouRym Worms — 2010

ESAIM: Probability and Statistics

The P.O.T. (Peaks-Over-Threshold) approach consists of using the Generalized Pareto Distribution (GPD) to approximate the distribution of excesses over a threshold. We use the probability-weighted moments to estimate the parameters of the approximating distribution. We study the asymptotic behaviour of these estimators (in particular their asymptotic bias) and also the functional bias of the GPD as an estimate of the distribution function of the excesses. We adapt penultimate approximation results...

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