Non-parametric quantile selection for extreme distributions

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)454-466
Number of pages13
JournalJournal of Modern Applied Statistical Methods
Volume7
Issue number2
Publication statusPublished - Nov 2008

Fingerprint

Quantile
Extremes
Quantile Estimation
Nonparametric Estimation
Moment Method
Kernel Estimator
Monte Carlo method
Parameter Estimation
Quantile estimation

Keywords

  • HD quantile
  • Imse.
  • Kernel quantile estimators
  • LQ-moments
  • Order statistics
  • Sample quantiles
  • Weighted HD quantile
  • Weighted kernel quantile estimators

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Cite this

Non-parametric quantile selection for extreme distributions. / Wan Zin @ Wan Ibrahim, Wan Zawiah; Jemain, Abdul Aziz.

In: Journal of Modern Applied Statistical Methods, Vol. 7, No. 2, 11.2008, p. 454-466.

Research output: Contribution to journalArticle

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