Extreme value statistics for censored data with heavy tails under competing risks
Julien Worms () and
Rym Worms ()
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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, 849-889
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)
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