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Extreme Value Inference for General Heterogeneous Data

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  • Einmahl, John

    (Tilburg University, Center For Economic Research)

  • He, Y.

    (Tilburg University, Center For Economic Research)

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Suggested Citation

  • Einmahl, John & He, Y., 2022. "Extreme Value Inference for General Heterogeneous Data," Discussion Paper 2022-017, Tilburg University, Center for Economic Research.
    • Handle: RePEc:tiu:tiucen:fd8dd91c-086f-40e6-ac29-3785bd0b56cd
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    References listed on IDEAS

    as
    1. John H. J. Einmahl & Yi He, 2022. "Extreme Value Estimation for Heterogeneous Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 255-269, December.
    2. Laurens de Haan & Chen Zhou, 2021. "Trends in Extreme Value Indices," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(535), pages 1265-1279, July.
    3. Einmahl, J. H.J. & Dekkers, A. L.M. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Other publications TiSEM 81970cb3-5b7a-4cad-9bf6-2, Tilburg University, School of Economics and Management.
    4. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2003. "A theory of power-law distributions in financial market fluctuations," Nature, Nature, vol. 423(6937), pages 267-270, May.
    5. Einmahl, J.H.J. & de Haan, L.F.M. & Zhou, C., 2014. "Statistics of Heteroscedastic Extremes," Other publications TiSEM 19952ae4-25ff-4e1b-8627-d, Tilburg University, School of Economics and Management.
    6. John H. J. Einmahl & Laurens Haan & Chen Zhou, 2016. "Statistics of heteroscedastic extremes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 31-51, January.
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