The objective is to select the best non-parametric quantile estimation method for extreme distributions. This serves as a starting point for further research in quantile application such as in parameter estimation using LQ-moments method. Thirteen methods of non-parametric quantile estimation were applied on six types of extreme distributions and their efficiencies compared. Monte Carlo methods were used to generate the results, which showed that the method of Weighted Kernel estimator of Type 1 was more efficient than the other methods in many cases.
Wan Zin, Wan Zawiah and Jemain, Abdul Aziz
"Non-Parametric Quantile Selection for Extreme Distributions,"
Journal of Modern Applied Statistical Methods:
2, Article 10.
Available at: http://digitalcommons.wayne.edu/jmasm/vol7/iss2/10