期刊
ENVIRONMETRICS
卷 14, 期 1, 页码 27-43出版社
WILEY-BLACKWELL
DOI: 10.1002/env.555
关键词
peaks-over-threshold method; extremes; bootstrap; simulations; uncertainty; L-moments; wind speed; return period; Pareto distribution; quantiles
In the peaks-over-threshold (POT) method of extreme quantile estimation, the selection of a suitable threshold is critical to estimation accuracy. In practical applications, however, the threshold selection is not so obvious due to erratic variation of quantile estimates with minor changes in threshold. To address this issue, the article investigates the variation of quantile uncertainty (bias and variance) as a function of threshold using a semiparametric bootstrap algorithm. Furthermore, the article compares the performance of L-moment and de Haan methods that are used for fitting the Pareto distribution to peak data. The analysis of simulated and actual U.S. wind speed data illustrates that the L-moment method can lead to almost unbiased quantile estimates for certain thresholds. A threshold corresponding to minimum standard error appears to provide reasonable estimates of wind speed extremes. It is concluded that the quantification of uncertainty associated with a quantile estimate is necessary for selecting a suitable threshold and estimating the design wind speed. For this purpose, semi-parametric bootstrap method has proved to be a simple, practical and effective tool. Copyright (C) 2003 John Wiley Sons, Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据