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Outlier identification and robust parameter estimation in a zero-inflated Poisson model

机译:零膨胀泊松模型中的离群值识别和鲁棒参数估计

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摘要

The Zero-inflated Poisson distribution has been used in the modeling of count data in different contexts. This model tends to be influenced by outliers because of the excessive occurrence of zeroes, thus outlier identification and robust parameter estimation are important for such distribution. Some outlier identification methods are studied in this paper, and their applications and results are also presented with an example. To eliminate the effect of outliers, two robust parameter estimates are proposed based on the trimmed mean and the Winsorized mean. Simulation results show the robustness of our proposed parameter estimates.
机译:零膨胀泊松分布已用于不同上下文中的计数数据建模。由于过多出现零,该模型容易受到异常值的影响,因此异常值的识别和可靠的参数估计对于这种分布很重要。本文研究了一些离群值识别方法,并举例说明了它们的应用和结果。为了消除离群值的影响,基于调整后的均值和Winsorized均值,提出了两个鲁棒的参数估计。仿真结果表明了我们提出的参数估计的鲁棒性。

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