Extreme value statistics for censored data with heavy tails under competing risks
Julien Worms (julien.worms@uvsq.fr) and
Rym Worms (rym.worms@u-pec.fr)
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Julien Worms: Université Paris-Saclay / Université de Versailles-Saint-Quentin-En-Yvelines, Laboratoire de Mathématiques de Versailles (CNRS UMR 8100)
Rym Worms: Université Paris-Est, Laboratoire d’Analyse et de Mathématiques Appliquées (CNRS UMR 8050)
Metrika: International Journal for Theoretical and Applied Statistics, 2018, vol. 81, issue 7, No 5, 849-889
Abstract:
Abstract This paper addresses the problem of estimating, from randomly censored data subject to competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in a heavy-tail framework. Asymptotic normality of the proposed estimator is established. This estimator has the form of an Aalen-Johansen integral and is the first estimator proposed in this context. Estimation of extreme quantiles of the cumulative incidence function is then addressed as a consequence. A small simulation study exhibits the performances for finite samples.
Keywords: Extreme value index; Tail inference; Random censoring; Competing Risks; Aalen-Johansen estimator; Primary 62G32; Secondary 62N02 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)
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Persistent link: https://EconPapers.repec.org/RePEc:spr:metrik:v:81:y:2018:i:7:d:10.1007_s00184-018-0662-3
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DOI: 10.1007/s00184-018-0662-3
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