Výsledky vyhledávání

Report

Estimation of the extreme value index in a censorship framework: asymptotic and finite sample behaviour

Autor: Beirlant, Jan, Worms, Julien, Worms, Rym

We revisit the estimation of the extreme value index for randomly censored data from a heavy tailed distribution. We introduce a new class of estimators which encompasses earlier proposals given in Worms and Worms (2014) and Beirlant et al. (2018), w

Externí odkaz: http://arxiv.org/abs/1804.06583

Zobrazit plný text záznamu

Report

Extreme value statistics for censored data with heavy tails under competing risks

Autor: Worms, Julien, Worms, Rym

This paper addresses the problem of estimating, in the presence of random censoring as well as competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in the heavy-tail case. Asymptotic normalit

Externí odkaz: http://arxiv.org/abs/1701.05458

Zobrazit plný text záznamu

Report

A Lynden-Bell integral estimator for extremes of randomly truncated data

Autor: Worms, Julien, Worms, Rym

This work deals with the estimation of the extreme value index and extreme quantiles for heavy tailed data,randomly right truncated by another heavy tailed variable. Under mild assumptions and the condition thatthe truncated variable is less heavy-ta

Externí odkaz: http://arxiv.org/abs/1507.04189

Zobrazit plný text záznamu

Report

Moment estimators of the extreme value index for randomly censored data in the Weibull domain of attraction

Autor: Worms, Julien, Worms, Rym

This paper addresses the problem of estimating the extreme value index in presence of random censoring for distributions in the Weibull domain of attraction. The methodologies introduced in [Worms (2014)], in the heavy-tailed case, are adapted here t

Externí odkaz: http://arxiv.org/abs/1506.03765

Zobrazit plný text záznamu

Report

Empirical Likelihood based Confidence Regions for first order parameters of a heavy tailed distribution

Autor: Worms, Julien, Worms, Rym

Let $X_1, \ldots, X_n$ be some i.i.d. observations from a heavy tailed distribution $F$, i.e. such that the common distribution of the excesses over a high threshold $u_n$ can be approximated by a Generalized Pareto Distribution $G_{\gamma,\sigma_n}$

Externí odkaz: http://arxiv.org/abs/1008.3229

Zobrazit plný text záznamu