ISSN:
1432-1343
Keywords:
Mixture models
;
Generalized linear models
;
EM algorithm
;
Maximum likelihood estimation
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mathematics
Notes:
Abstract A mixture model approach is developed that simultaneously estimates the posterior membership probabilities of observations to a number of unobservable groups or latent classes, and the parameters of a generalized linear model which relates the observations, distributed according to some member of the exponential family, to a set of specified covariates within each Class. We demonstrate how this approach handles many of the existing latent class regression procedures as special cases, as well as a host of other parametric specifications in the exponential family heretofore not mentioned in the latent class literature. As such we generalize the McCullagh and Nelder approach to a latent class framework. The parameters are estimated using maximum likelihood, and an EM algorithm for estimation is provided. A Monte Carlo study of the performance of the algorithm for several distributions is provided, and the model is illustrated in two empirical applications.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01202266
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