Statistics and Computing

, Volume 6, Issue 3, pp 251–262

A general maximum likelihood analysis of overdispersion in generalized linear models

  • Murray Aitkin

DOI: 10.1007/BF00140869

Cite this article as:
Aitkin, M. Stat Comput (1996) 6: 251. doi:10.1007/BF00140869


This paper presents an EM algorithm for maximum likelihood estimation in generalized linear models with overdispersion. The algorithm is initially derived as a form of Gaussian quadrature assuming a normal mixing distribution, but with only slight variation it can be used for a completely unknown mixing distribution, giving a straightforward method for the fully non-parametric ML estimation of this distribution. This is of value because the ML estimates of the GLM parameters may be sensitive to the specification of a parametric form for the mixing distribution. A listing of a GLIM4 algorithm for fitting the overdispersed binomial logit model is given in an appendix.

A simple method is given for obtaining correct standard errors for parameter estimates when using the EM algorithm.

Several examples are discussed.


Overdispersionrandom effects GLMEM algorithmmixture model

Copyright information

© Chapman & Hall 1996

Authors and Affiliations

  • Murray Aitkin
    • 1
  1. 1.Department of MathematicsUniversity of Western AustraliaNedlandsAustralia