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Simulation On Poisson And Negative Binomial Models Of Count Road Accident Modeling

机译:县城事故造型泊松与负二型模型的仿真

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Accident count data have often been shown to have overdispersion. On the other hand, the data might contain zero count (excess zeros). The simulation study was conducted to create a scenarios which an accident happen in Tjunction with the assumption the dependent variables of generated data follows certain distribution namely Poisson and negative binomial distribution with different sample size of n=30 to n=500. The study objective was accomplished by fitting Poisson regression, negative binomial regression and Hurdle negative binomial model to the simulated data. The model validation was compared and the simulation result shows for each different sample size, not all model fit the data nicely even though the data generated from its own distribution especially when the sample size is larger. Furthermore, the larger sample size indicates that more zeros accident count in the dataset.
机译:事故计数数据经常被证明具有过度分解。另一方面,数据可能包含零计数(多余零)。进行了仿真研究以创建一种情况,在T函数中发生事故的情况,假设产生的数据的相关变量遵循某些分布即泊松和负二进制分布,其不同的样品大小为n = 30至n = 500。该研究目标是通过拟合泊松回归,负二项式回归和障碍负二进制模型来实现的模拟数据来实现。比较模型验证,并且对于每个不同的样本大小,仿真结果表明,即使当样本大小较大时,也不会使数据均匀地拟合数据。此外,更大的样本大小表示数据集中的零事故计数更多。

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