In this paper, we consider the use of the EM algorithm for the fitting of distributions by maximum likelihood to overdispersed count data. In the course of this, we also provide a review of various approaches that have been proposed for the analysis of such data. As the Poisson and binomial regression models, which are often adopted in the first instance for these analyses, are particular examples of a generalized linear model (GLM), the focus of the account is on the modifications and extensions to GLMs for the handling of overdispersed count data.

译文

在本文中,我们考虑使用EM算法通过最大似然拟合过度分散的计数数据来拟合分布。在此过程中,我们还将回顾已提出的用于分析此类数据的各种方法。由于通常首先用于这些分析的泊松和二项式回归模型是广义线性模型(GLM)的特定示例,因此该帐户的重点是对GLM的修改和扩展,以处理过度分散的模型。计数数据。

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