关于用于过度分散计数数据的期望最大化（EM）算法。

On the EM algorithm for overdispersed count data.

作者信息

McLachlan G J

机构信息

Department of Mathematics, University of Queensland, Australia.

出版信息

Stat Methods Med Res. 1997 Mar;6(1):76-98. doi: 10.1177/096228029700600106.

DOI:10.1177/096228029700600106

PMID:9185291

Abstract

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.

摘要

在本文中，我们考虑使用期望最大化（EM）算法，通过最大似然法来拟合过度分散计数数据的分布。在此过程中，我们还综述了针对此类数据的分析所提出的各种方法。由于泊松回归模型和二项回归模型通常在这些分析中首先被采用，它们是广义线性模型（GLM）的特殊例子，因此本文的重点是对广义线性模型进行修改和扩展，以处理过度分散计数数据。