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Detecting influential data points for the Hill estimator in Pareto-type distributions

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  • Hubert, Mia
  • Dierckx, Goedele
  • Vanpaemel, Dina

Abstract

Pareto-type distributions are extreme value distributions for which the extreme value index γ>0. Classical estimators for γ>0, like the Hill estimator, tend to overestimate this parameter in the presence of outliers. The empirical influence function plot, which displays the influence that each data point has on the Hill estimator, is introduced. To avoid a masking effect, the empirical influence function is based on a new robust GLM estimator for γ. This robust GLM estimator is used to determine high quantiles of the data generating distribution, allowing to flag data points as unusually large if they exceed this high quantile.

Suggested Citation

  • Hubert, Mia & Dierckx, Goedele & Vanpaemel, Dina, 2013. "Detecting influential data points for the Hill estimator in Pareto-type distributions," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 13-28.
  • Handle: RePEc:eee:csdana:v:65:y:2013:i:c:p:13-28
    DOI: 10.1016/j.csda.2012.07.011
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    References listed on IDEAS

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    Cited by:

    1. Asimit, Alexandru V. & Badescu, Alexandru M. & Verdonck, Tim, 2013. "Optimal risk transfer under quantile-based risk measurers," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 252-265.
    2. Michal Brzezinski, 2016. "Robust estimation of the Pareto tail index: a Monte Carlo analysis," Empirical Economics, Springer, vol. 51(1), pages 1-30, August.

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