A general maximum likelihood analysis of overdispersion in generalized linear models
- 1 September 1996
- journal article
- Published by Springer Nature in Statistics and Computing
- Vol. 6 (3) , 251-262
- https://doi.org/10.1007/bf00140869
Abstract
No abstract availableKeywords
This publication has 25 references indexed in Scilit:
- A hybrid EM/Gauss-Newton algorithm for maximum likelihood in mixture distributionsStatistics and Computing, 1996
- Probability model choice in single samples from exponential families using Poisson log-linear modelling, and model comparison using Bayes and posterior Bayes factorsStatistics and Computing, 1995
- Testing for Overdispersion in Poisson and Binomial Regression ModelsJournal of the American Statistical Association, 1992
- Tests of Hypotheses in Overdispersed Poisson Regression and other Quasi-Likelihood ModelsJournal of the American Statistical Association, 1990
- SOME MODELS FOR OVERDISPERSED BINOMIAL DATAAustralian Journal of Statistics, 1988
- Random effects in generalized linear models and the em algorithamCommunications in Statistics - Theory and Methods, 1988
- Double Exponential Families and Their Use in Generalized Linear RegressionJournal of the American Statistical Association, 1986
- Extra-Poisson Variation in Log-Linear ModelsJournal of the Royal Statistical Society Series C: Applied Statistics, 1984
- Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM AlgorithmPsychometrika, 1981
- Mixture Models, Outliers, and the EM AlgorithmTechnometrics, 1980