On robust tail index estimation

Jan Beran and Dieter Schell

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3430-3443

Abstract: A new approach to tail index estimation based on huberization of the Pareto MLE is considered. The proposed estimator is robust in a nonstandard way in that it protects against deviations from the central model at low quantiles. Asymptotic normality with the parametric n-rate of convergence is obtained with a bounded asymptotic bias under deviations from the Pareto model. The method is particularly useful for small samples where Hill-type estimators tend to be highly volatile. This is illustrated by a simulation study with sample sizes n≤100.

Keywords: Tail index estimation; Robust estimation; Huberization; Hill estimator; Influence functional; Small sample (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3430-3443

DOI: 10.1016/j.csda.2010.05.028

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