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• W2100070196 abstract "In the case of complete data, weak approximations to tail empirical processes for heavy-tailed distributions have been established by many authors. In this paper, we consider the random censoring setting through a tail Kaplan-Meier process and define a new estimator of the extreme value index. Under mild conditions, we establish the asymptotic normality of the proposed estimator and, through a simulation study, we investigate its performance and compare it to the adapted Hill estimator introduced by Einmahl, Fils-Villetard and Guillou, 2008." @default.
• W2100070196 created "2016-06-24" @default.
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• W2100070196 date "2015-02-13" @default.
• W2100070196 modified "2023-09-27" @default.
• W2100070196 title "A tail Kaplan-Meier empirical process with application to extreme-value based estimation under random censoring" @default.
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• W2100070196 hasPublicationYear "2015" @default.
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