假设违背的贝叶斯跨栏泊松回归

Nur Kamilah Sa'diyah, A. Astuti, M. B. Mitakda
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引用次数: 0

摘要

违反泊松回归假设会导致所形成的模型产生无偏估计量。对于小样本量和所有分布,有一种很好的估计参数的方法,即贝叶斯方法。慢性丝虫病数据的死亡人数违反泊松回归假设,因此采用贝叶斯障碍泊松回归建模。使用贝叶斯方法,在30万次迭代和7次稀疏的情况下完成收敛。结果表明,在logit模型中,只有一个预测变量对印度尼西亚34个省因慢性丝虫病而死亡的病例数有显著影响。截断泊松模型导致所有预测变量对慢性丝虫病死亡病例的数量有显著影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Hurdle Poisson Regression for Assumption Violation
Violation of the Poisson regression assumption can cause the model formed will produce an unbiased estimator. There is a good method for estimating parameters on small sample sizes and on all distributions, namely the Bayesian method. The number of death from chronic Filariasis data violates the Poisson regression assumption, so it is modeled with the Bayesian Hurdle Poisson Regression. With the Bayesian method, convergence is fullfilled when 300000 iterations and 7 thin are performed. The results showed that in the logit model only one predictor variable had a significant effect on the number of cases of death due to chronic Filiariasis in 34 Provinces in Indonesia . The Truncated Poisson model resulted in all predictor variables having a significant effect on the number of cases of death due to chronic Filariasis.
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