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.
Original language | English (US) |
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Pages (from-to) | 21-55 |
Number of pages | 35 |
Journal | Journal of Classification |
Volume | 12 |
Issue number | 1 |
DOIs | |
State | Published - Mar 1995 |
All Science Journal Classification (ASJC) codes
- Mathematics (miscellaneous)
- Psychology (miscellaneous)
- Statistics, Probability and Uncertainty
- Library and Information Sciences