Abstract
In this paper, a family of Finite Mixed Generalized Linear Models is considered. A straightforward general EM-algorithm for estimating any model from this family by standard GLM-software is given. After discussing the particular problems of statistical inference arising when FMGLMs are used, three estimators of standard errors of the parameter estimates are compared by means of example data and some simulations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aitkin (1994) An EM algorithm for overdispersion in generalized linear models. Proceedings of the 9th International Workshop on Statistical Modelling Exeter 1994
Böhning, D., Schlattmann, P., and B. Lindsay (1992) Computer Assisted Analysis of Mixtures (C.A.MAN): Statistical Algorithms. Biometrics, 48, 283–303
Böhning, D., Dietz, E., Schlattmann, P. and Lindsay, B.G. (1994) The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family. Annals of the Institute of Statistical Mathematics., 46, 373–388
Celeux, G. and J. Diebolt (1985) The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for mixture problems. Computational Statistics Quarterly, 2 1
Dietz, E. (1995) Estimating standard errors of finite mixtures models by MCEM-steps and multiple imputations. The Annals of Statistics to appear
Jansen, R.C. (1993) Maximum likelihood in a generalized linear finite mix-ture model by using the EM algorithm. Biometrics 49, 227–232
Lambert, D. (1992) Zero-inflated poisson regression, with an application to defects in manufacturing. Technometrics, 34, 1–14.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer Science+Business Media New York
About this paper
Cite this paper
Dietz, E., Böhning, D. (1995). Statistical Inference Based on a General Model of Unobserved Heterogeneity. In: Seeber, G.U.H., Francis, B.J., Hatzinger, R., Steckel-Berger, G. (eds) Statistical Modelling. Lecture Notes in Statistics, vol 104. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0789-4_10
Download citation
DOI: https://doi.org/10.1007/978-1-4612-0789-4_10
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94565-1
Online ISBN: 978-1-4612-0789-4
eBook Packages: Springer Book Archive