title: On the EM algorithm for overdispersed count data.
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.
authors.affiliation: Department of Mathematics, University of Queensland, Australia. gjm@maths.uq.edu.au
authors.forename: G J
authors.initials: GJ
authors.lastname: McLachlan
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001699:Biometry / Q000379:methods*; D004812:Epidemiologic Methods; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D016015:Logistic Models; D015233:Models, Statistical; D016012:Poisson Distribution