A simulation-based hyperparameter selection for quantile estimation of the generalized extreme value distribution

被引:17
作者
Park, JS [1 ]
机构
[1] Chonnam Natl Univ, Dept Stat, Kwangju 500757, South Korea
基金
新加坡国家研究基金会;
关键词
beta distribution; hydrology; maximum likelihood estimation; L-moment estimation; penalized likelihood; shape parameter;
D O I
10.1016/j.matcom.2005.09.003
中图分类号
TP39 [计算机的应用];
学科分类号
081203 [计算机应用技术]; 0835 [软件工程];
摘要
A systematic way of selecting hyperparameters of the prior on the shape parameter of the generalized extreme value distribution (GEVD) is presented. The optimal selection is based on a Monte Carlo simulation in the generalized maximum likelihood estimation (GMLE) framework. A scaled total misfit measure for the accurate estimation of upper quantiles is used for the selection criterion. The performance evaluations for GEVD and non-GEVD show that the GMLE with selected hyperparameters produces more accurate quantile estimates than the MLE, the L-moments estimator, and Martins-Stedinger's GMLE. (c) 2005 IMACS. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:227 / 234
页数:8
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