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Performances of some parameter estimators of the generalized Pareto distribution over rounded-off samples

机译:四舍五入样本上广义Pareto分布的一些参数估计量的性能

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Recent analyses on some daily rainfall time series highlighted the presence of records with anomalous rounding (1 and 5 mm), while the standard resolution should be 0.1 or 0.2 mm. Assuming that the generalized Pareto distribution (GPD) can reliably represent the distribution of daily rainfall depths, this study investigates how such discretizations can affect the inference process. The performances of several GPD estimators are compared using the Monte Carlo approach. Synthetic samples are drawn by GPDs with shape and scale parameters in the ranges of values estimated on daily rainfall depth time series. Results show how the relative efficiency of estimators could be very different for continuous or rounded-off samples. Moreover, when the rounding-off magnitude becomes larger than a few millimeters, all the considered estimators reveal very poor performances.
机译:最近对一些每日降雨时间序列的分析强调指出,存在舍入异常(1和5 mm)的记录,而标准分辨率应为0.1或0.2 mm。假设广义帕累托分布(GPD)可以可靠地表示每日降雨深度的分布,则本研究调查了这种离散化如何影响推断过程。使用蒙特卡洛方法比较了几个GPD估计器的性能。通过GPD在形状和比例参数上提取合成样品,这些形状和比例参数在每日降雨深度时间序列上估算的值范围内。结果表明,对于连续或四舍五入的样本,估计量的相对效率可能有很大不同。而且,当舍入幅度大于几毫米时,所有考虑的估计量都显示出非常差的性能。

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