A MIXTURE LIKELIHOOD APPROACH FOR GENERALIZED LINEAR-MODELSWEDEL, M. & DESARBO, WS., 1995, In : Journal of Classification. 12, 1, p. 21-55 35 p.
Research output: Contribution to journal › Article › Academic › peer-review
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.
|Number of pages||35|
|Journal||Journal of Classification|
|Publication status||Published - 1995|
- MIXTURE MODELS, GENERALIZED LINEAR MODELS, EM ALGORITHM, MAXIMUM LIKELIHOOD ESTIMATION, MAXIMUM-LIKELIHOOD, EM ALGORITHM, NORMAL-DISTRIBUTIONS, INFORMATION CRITERION, SWITCHING REGRESSIONS, CONVERGENCE, METHODOLOGY, PARAMETERS, SELECTION, CONSUMER