Generalized maximum‐likelihood generalized extreme‐value quantile estimators for hydrologic data

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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 recommendLmoment estimators. More recent research shows that method of moments quantile estimators have for −0.25 < κ < 0.30 smaller root‐mean‐square error thanLmoments 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 andLmoment quantile estimators for − 0.4 ≤ κ ≤ 0.