A general EM approach for maximum likelihood estimation in mixed Poisson regression models
A general EM approach for maximum likelihood estimation in mixed Poisson regression models
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DOI：
10.1177/1471082x0100100405
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发表时间：
2001-12
期刊：
影响因子：
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通讯作者：
D. Karlis
中科院分区：
文献类型：
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作者：
D. Karlis
针对混合泊松回归模型，提出了一种EM型的最大似然估计算法。该算法利用了这些模型的混合表示。本文对该家族的两个成员——负二项回归模型和泊松-逆高斯回归模型进行了深入的研究。推导了两种模型的封闭表达式，使算法易于编程。特别是对于泊松-逆高斯模型，不需要特殊的数值技术。该算法应用于希腊犯罪数据的真实数据集。
An EM type algorithm for maximum likelihood estimation is proposed for the case of mixed Poisson regression models. The algorithm makes use of the mixture representation of such models. Two members of this family are examined in depth, the negative binomial regression model and the Poisson-inverse Gaussian regression model. Closed form expressions are derived for both models leading to easily programmable algorithms. Especially for the case of the Poisson-inverse Gaussian model no special numerical techniques are needed. The algorithms are applied to a real data set concerning crime data from Greece.