Extreme value statistics for censored data with heavy tails under competing risks

2017/01/19 by Julien Worms, Worms, Julien, Rym Worms +1
Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Financial Risk and Volatility Modeling #Machine Learning (stat.ML) #Statistical Distribution Estimation and Applications #Statistical Methods and Inference #Statistics Theory (math.ST)

paper · doi:10.48550/arxiv.1701.05458

openalex publication_date 2017/01/19 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28

Abstract

This paper addresses the problem of estimating, in the presence of random censoring as well as competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in the heavy-tail case. Asymptotic normality of the proposed estimator (which has the form of an Aalen-Johansen integral, and is the first estimator proposed in this context) is established. A small simulation study exhibits its performances for finite samples. Estimation of extreme quantiles of the cumulative incidence function is also addressed.

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