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- Title
Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data.
- Authors
Martins, Eduardo S.; Stedinger, Jery R.
- Abstract
The three-parameter generalized extreme-value (GEV) distribution has found wide application for describing annual floods, rainfall, wind speeds, wave heights, snow depths, and other maxima. Previous studies show that small-sample maximum-likelihood estimators (MLE) of parameters are unstable and recommend L moment estimators. More recent research shows that method of moments quantile estimators have for −0.25 < κ < 0.30 smaller root-mean-square error than L moments and MLEs. Examination of the behavior of MLEs in small samples demonstrates that absurd values of the GEV-shape parameter κ can be generated. Use of a Bayesian prior distribution to restrict κ values to a statistically/physically reasonable range in a generalized maximum likelihood (GML) analysis eliminates this problem. In our examples the GML estimator did substantially better than moment and L moment quantile estimators for − 0.4 ≤ κ ≤ 0.
- Publication
Water Resources Research, 2000, Vol 36, Issue 3, p737
- ISSN
0043-1397
- Publication type
Article
- DOI
10.1029/1999WR900330