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Alternative Method for Parametric Estimation of Generalised Extreme Value Distribution

机译:广义极值分布的参数估计的替代方法

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Topping extreme teritlak been used so broadly to describe the annual floods, rain, speed of wind, wave height, depth salji and other extreme events. In this paper a polynomial regression model estimates the parameters introduced in the form of sprinkles sprinkles teritlak extreme value (GEV) for use in flood frequency analysis. Three polynomial regression model estimates the parameters introduced in this. Monte Carlo simulation is used to explain keupayaan polynomial regression models compared to polynomial models that are reserved by Hosking et al. [5]. The results of the study indicate rank polynomial regression models are generally better 5 versus other models, and these models make the best choice for topping estimates the parameters of GEV.Keywords: polynomial regression; L-moments; GEV; repeat the entire period.
机译:极端teritlak的顶部被广泛地用来描述年度洪水,降雨,风速,浪高,深度salji和其他极端事件。在本文中,一个多项式回归模型可以估算以洒水量和洒水量极值(GEV)形式引入的参数,用于洪水频率分析。三个多项式回归模型可估算在此引入的参数。与Hosking等人保留的多项式模型相比,蒙特卡罗模拟用于解释keupayaan多项式回归模型。 [5]。研究结果表明,等级多项式回归模型通常比其他模型更好5,这些模型是对GEV参数进行估算的最佳选择。 L-矩GEV;重复整个过程。

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