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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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    Cited by:

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    2. 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.
    3. Gareth W. Peters & Matteo Malavasi & Georgy Sofronov & Pavel V. Shevchenko & Stefan Trück & Jiwook Jang, 2023. "Cyber loss model risk translates to premium mispricing and risk sensitivity," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 48(2), pages 372-433, April.
    4. Nasir Abbas & Mu’azu Ramat Abujiya & Muhammad Riaz & Tahir Mahmood, 2020. "Cumulative Sum Chart Modeled under the Presence of Outliers," Mathematics, MDPI, vol. 8(2), pages 1-30, February.
    5. Arthur Charpentier & Emmanuel Flachaire, 2019. "Pareto Models for Top Incomes," Working Papers hal-02145024, HAL.
    6. Gareth W. Peters & Matteo Malavasi & Georgy Sofronov & Pavel V. Shevchenko & Stefan Truck & Jiwook Jang, 2022. "Cyber Loss Model Risk Translates to Premium Mispricing and Risk Sensitivity," Papers 2202.10588, arXiv.org, revised Mar 2023.
    7. Yuri Goegebeur & Armelle Guillou & Jing Qin, 2023. "Robust estimation of the conditional stable tail dependence function," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 201-231, April.

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