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

被引：16
|
作者：
Park, JS [1 ]
机构：
[1] Chonnam Natl Univ, Dept Stat, Kwangju 500757, South Korea
来源：
MATHEMATICS AND COMPUTERS IN SIMULATION | 2005年 / 70卷 / 04期
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 ; 0835 ;
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
相关论文
共 50 条
12345