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Extreme Value Inference for General Heterogeneous Data

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  • He, Yi
  • Einmahl, John

    (Tilburg University, School of Economics and Management)

Abstract

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  • He, Yi & Einmahl, John, 2024. "Extreme Value Inference for General Heterogeneous Data," Other publications TiSEM 5d01cb7e-d528-406d-8c24-c, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:5d01cb7e-d528-406d-8c24-c004b13014bb
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    File URL: https://pure.uvt.nl/ws/portalfiles/portal/92623684/2024-014.pdf
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    References listed on IDEAS

    as
    1. 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.
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    1. Einmahl, John & He, Y., 2022. "Extreme Value Inference for General Heterogeneous Data," Discussion Paper 2022-017, Tilburg University, Center for Economic Research.
    2. Einmahl, John & He, Y., 2022. "Extreme Value Inference for General Heterogeneous Data," Other publications TiSEM fd8dd91c-086f-40e6-ac29-3, Tilburg University, School of Economics and Management.
    3. Natalia Markovich & Marijus Vaičiulis, 2023. "Extreme Value Statistics for Evolving Random Networks," Mathematics, MDPI, vol. 11(9), pages 1-35, May.

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