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The study of the extreme values of a variable such as wave height is very important in flood risk assessment and coastal design. Often values above a sufficiently large threshold can be modelled using the Generalized Pareto Distribution, the parameters of which are estimated using maximum likelihood. There are several popular empirical techniques for choosing a suitable threshold, but these require the subjective interpretation of plots by the user.In this paper we present a pragmatic automated, simple and computationally inexpensive threshold selection method based on the distribution of the difference of parameter estimates when the threshold is changed, and apply it to a published rainfall and a new wave height data set. We assess the effect of the uncertainty associated with our threshold selection technique on return level estimation by using the bootstrap procedure. We illustrate the effectiveness of our methodology by a simulation study and compare it with the approach used in the JOINSEA software. In addition, we present an extension that allows the threshold selected to depend on the value of a covariate such as the cosine of wave direction.  相似文献
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以CCMP风场(1988年1月~2010年12月)为驱动,本文采用第三代海浪数值模式SWAN对中国海海区进行风浪场的数值模拟,并利用浮标资料对驱动风场和计算有效波高进行验证。根据模拟的结果分别采用广义帕雷托分布(GPD)和常用的广义极值分布(GEV)拟合累积频率曲线并进行效果检验,进而分析和比较2种分布的优缺点,并运用广义帕雷托分布(GPD)模型进行重现期波要素的推算,分析GPD模型的应用前景。  相似文献
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