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A level crossing quantile estimation method

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  • Huang, Mei Ling
  • Brill, Percy

Abstract

We introduce a nonparametric quantile estimation method by applying a level crossing empirical function which will be defined in this paper, and also introduce a computational method for the new estimator. A comparison of the new quantile estimation method with the usual kernel quantile estimation method based on the classical empirical distribution function is included. Computational results show that the new method is more efficient than the usual method in many cases.

Suggested Citation

  • Huang, Mei Ling & Brill, Percy, 1999. "A level crossing quantile estimation method," Statistics & Probability Letters, Elsevier, vol. 45(2), pages 111-119, November.
    • Handle: RePEc:eee:stapro:v:45:y:1999:i:2:p:111-119
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    Citations

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    Cited by:

    1. Maria E. Frey & Hans C. Petersen & Oke Gerke, 2020. "Nonparametric Limits of Agreement for Small to Moderate Sample Sizes: A Simulation Study," Stats, MDPI, vol. 3(3), pages 1-13, August.
    2. Marta Małecka & Dorota Pekasiewicz, 2013. "A Modification of the Probability Weighted Method of Moments and its Application to Estimate the Financial Return Distribution Tail," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(3), pages 495-506, September.
    3. Huang, Mei Ling, 2001. "On a distribution-free quantile estimator," Computational Statistics & Data Analysis, Elsevier, vol. 37(4), pages 477-486, October.

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