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Estimation of extremes for heavy-tailed and light-tailed distributions in the presence of random censoring

Abstract : In this paper, we use the flexible semi-parametric model $A_1(\tau,\theta)$ introduced in Gardes-Girard-Guillou (2011) for estimating extremes of censored data. Both the censored and the censoring variables are supposed to belong to this family of distributions. Solutions for modeling the tail of censored data which are between Weibull-tail and Pareto-tail behavior are considered. Estimators of the parameters, as well as high-quantiles, are proposed and asymptotic normality results are proved. Various combinations of the tails of censored and censoring distributions are covered, ranging from rather light censoring to severe censoring in the tail.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-03046969
Contributor : Julien Worms <>
Submitted on : Tuesday, December 8, 2020 - 4:25:06 PM
Last modification on : Saturday, December 12, 2020 - 3:56:54 AM

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  • HAL Id : hal-03046969, version 1

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Julien Worms, Rym Worms. Estimation of extremes for heavy-tailed and light-tailed distributions in the presence of random censoring. 2020. ⟨hal-03046969⟩

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