Estimation of the generalized extreme-value distribution by the method of probability-weighted moments

J. R.M. Hosking, J. R. Wallis, Eric F. Wood

Research output: Contribution to journalArticle

825 Scopus citations

Abstract

We use the method of probability-weighted moments to derive estimators of the parameters and quantiles of the generalized extreme-value distribution. We investigate the properties of these estimators in large samples, via asymptotic theory, and in small and moderate samples, via computer simulation. Probability-weighted moment estimators have low variance and no severe bias, and they compare favorably with estimators obtained by the methods of maximum likelihood or sextiles. The method of probability-weighted moments also yields a convenient and powerful test of whether an extreme-value distribution is of Fisher-Tippett Type I, II, or III.

Original languageEnglish (US)
Pages (from-to)251-261
Number of pages11
JournalTechnometrics
Volume27
Issue number3
DOIs
StatePublished - Aug 1985

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

Keywords

  • Generalized extreme-value distribution
  • Hypothesis testing
  • Order statistics
  • Probability-weighted moments

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