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Estimation of extremes for Weibull-tail distributions in the presence of random censoring

Abstract : The Weibull-tail class of distributions is a sub-class of the Gumbel extreme domain of attraction, and it has caught the attention of a number of researchers in the last decade, particularly concerning the estimation of the so-called Weibull-tail coefficient. In this paper, we propose an estimator of this Weibull-tail coefficient when the Weibull-tail distribution of interest is censored from the right by another Weibull-tail distribution: to the best of our knowledge, this is the first one proposed in this context. A corresponding estimator of extreme quantiles is also proposed. In both mild censoring and heavy censoring (in the tail) settings, asymptotic normality of these estimators is proved, and their finite sample behavior is presented via some simulations.
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https://hal.archives-ouvertes.fr/hal-02024397
Contributor : Rym Worms <>
Submitted on : Tuesday, February 19, 2019 - 10:33:08 AM
Last modification on : Monday, July 5, 2021 - 2:16:25 PM
Long-term archiving on: : Monday, May 20, 2019 - 3:00:56 PM

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Julien Worms, Rym Worms. Estimation of extremes for Weibull-tail distributions in the presence of random censoring. Extremes, Springer Verlag (Germany), 2019, 22 (4), p667-704. ⟨10.1007/s10687-019-00354-2⟩. ⟨hal-02024397⟩

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