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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
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
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
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
Publikováno v:
In Journal of Statistical Planning and Inference September 2019 202:31-56
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
Autor:
Worms, Julien1,2 (AUTHOR), Worms, Rym3 (AUTHOR) rym.worms@u-pec.fr
Publikováno v:
Statistics. Oct 2021, Vol. 55 Issue 5, p979-1017. 39p.
International audience; In the context of climate change, assessing how likely a particular change or event has been caused by human influence is important for mitigation and adaptation policies. In this work we propose an extreme event attribution (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::58bc58be18916790a20dfa2e58291c5e
https://hal.science/hal-04006516/document
https://hal.science/hal-04006516/document
Akademický článek
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Autor:
Worms, Julien, Worms, Rym
Publikováno v:
In Journal of Statistical Planning and Inference 2011 141(8):2769-2786