Abstract
The Weibull tail coefficient (WTC) is the parameter \(\theta \) in a right-tail function of the type \(\overline{F}:=1-F\), such that \(H:=-\ln \overline{F}\) is a regularly varying function at infinity with an index of regular variation equal to \(\theta \in \mathbb {R}^{+}\). In a context of extreme value theory for maxima, it is possible to prove that we have an extreme value index (EVI) \(\xi =0\), but usually a very slow rate of convergence. Most of the recent WTC-estimators are proportional to the class of Hill EVI-estimators, the average of the log-excesses associated with the k upper order statistics, \(1\le k<n\). The interesting performance of EVI-estimators based on generalized means leads us to base the WTC-estimation on the power mean-of-order-p (MO\(_p\)) EVI-estimators. Consistency of the WTC-estimators is discussed and their performance, for finite samples, is illustrated through a small-scale Monte Carlo simulation study.
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Acknowledgements
The research was partially supported by National Funds through FCT—Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) through projects UIDB/00297/2020 (CMA/UNL), UIDB/00006/2020 (CEA/UL) and UIDB/04674/2020 (CIMA).
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Caeiro, F., Gomes, M.I., Henriques-Rodrigues, L. (2022). Estimation of the Weibull Tail Coefficient Through the Power Mean-of-Order-p. In: Bispo, R., Henriques-Rodrigues, L., Alpizar-Jara, R., de Carvalho, M. (eds) Recent Developments in Statistics and Data Science. SPE 2021. Springer Proceedings in Mathematics & Statistics, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-031-12766-3_4
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