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Empirical likelihood method for intermediate quantiles

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  • Li, Zhouping
  • Gong, Yun
  • Peng, Liang

Abstract

Intermediate quantiles play an important role in the statistics of extremes with particular applications in risk management. For interval estimation of quantiles, Chen and Hall (1993) proposed the so-called smoothed empirical likelihood method. In this paper, we apply the method in Chen and Hall (1993) to construct confidence intervals for an intermediate quantile by deriving the corresponding Wilks Theorem.

Suggested Citation

  • Li, Zhouping & Gong, Yun & Peng, Liang, 2010. "Empirical likelihood method for intermediate quantiles," Statistics & Probability Letters, Elsevier, vol. 80(11-12), pages 1022-1029, June.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:11-12:p:1022-1029
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    References listed on IDEAS

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    1. 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.
    2. Csorgo, Miklos & Steinebach, Josef, 1991. "On the estimation of the adjustment coefficient in risk theory via intermediate order statistics," Insurance: Mathematics and Economics, Elsevier, vol. 10(1), pages 37-50, March.
    3. L. Viharos, 1997. "Tail index estimation based on linear combinations of intermediate order statistics," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 51(2), pages 164-177, July.
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    Cited by:

    1. Wang, Xing & Peng, Liang, 2016. "Inference for intermediate Haezendonck–Goovaerts risk measure," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 231-240.

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