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Tail inference using extreme U-statistics

Author

Listed:
  • Oorschot, Jochem

    (Erasmus University Rotterdam)

  • Segers, Johan

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Zhou, Chen

    (Erasmus University Rotterdam)

Abstract

Extreme U-statistics arise when the kernel of a U-statistic has a high degree but depends only on its arguments through a small number of top order statistics. As the kernel degree of the U-statistic grows to infinity with the sample size, estimators built out of such statistics form an intermediate family in between those constructed in the block maxima and peaks-over-threshold frameworks in extreme value analysis. The asymptotic normality of extreme U-statistics based on location-scale invariant kernels is established. Although the asymptotic variance coincides with the one of the Hájek projection, the proof goes beyond considering the first term in Hoeffding’s variance decomposition. We propose a kernel depending on the three highest order statistics leading to a location-scale invariant estimator of the extreme value index resembling the Pickands estimator. This extreme Pickands U-estimator is asymptotically normal and its finite-sample performance is competitive with that of the pseudo-maximum likelihood estimator.

Suggested Citation

  • Oorschot, Jochem & Segers, Johan & Zhou, Chen, 2023. "Tail inference using extreme U-statistics," LIDAM Reprints ISBA 2023006, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2023006
    DOI: https://doi.org/10.1214/23-EJS2129
    Note: In: Electronic Journal of Statistics, 2023, vol. 17(1), p. 1113-1159
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