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High quantile regression for extreme events

Author

Listed:
  • Mei Ling Huang

    (Brock University)

  • Christine Nguyen

    (Brock University)

Abstract

For extreme events, estimation of high conditional quantiles for heavy tailed distributions is an important problem. Quantile regression is a useful method in this field with many applications. Quantile regression uses an L 1-loss function, and an optimal solution by means of linear programming. In this paper, we propose a weighted quantile regression method. Monte Carlo simulations are performed to compare the proposed method with existing methods for estimating high conditional quantiles. We also investigate two real-world examples by using the proposed weighted method. The Monte Carlo simulation and two real-world examples show the proposed method is an improvement of the existing method.

Suggested Citation

  • Mei Ling Huang & Christine Nguyen, 2017. "High quantile regression for extreme events," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-20, December.
    • Handle: RePEc:spr:jstada:v:4:y:2017:i:1:d:10.1186_s40488-017-0058-3
    DOI: 10.1186/s40488-017-0058-3
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    References listed on IDEAS

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    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, September.
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    Cited by:

    1. E. Fusco & R. Benedetti & F. Vidoli, 2023. "Stochastic frontier estimation through parametric modelling of quantile regression coefficients," Empirical Economics, Springer, vol. 64(2), pages 869-896, February.
    2. Mei Ling Huang & Christine Nguyen, 2018. "A nonparametric approach for quantile regression," Journal of Statistical Distributions and Applications, Springer, vol. 5(1), pages 1-14, December.

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