A Mixture Likelihood Approach for Generalized Linear Models

Journal of Classification, Volume 12, Issue 1, pp 21-55

35 Pages Posted: 8 Jun 2016

See all articles by Michel Wedel

Michel Wedel

University of Maryland - Robert H. Smith School of Business, Marketing Department; University of Groningen - Faculty of Economics and Business

Wayne S. DeSarbo

Pennsylvania State University

Date Written: March 1995

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.

Keywords: Mixture models, Generalized linear models, EM algorithm, Maximum likelihood estimation

Suggested Citation

Wedel, Michel and DeSarbo, Wayne S., A Mixture Likelihood Approach for Generalized Linear Models (March 1995). Journal of Classification, Volume 12, Issue 1, pp 21-55. Available at SSRN: https://ssrn.com/abstract=2791031

Michel Wedel

University of Maryland - Robert H. Smith School of Business, Marketing Department ( email )

College Park, MD 20742
United States
301.405.2162 (Phone)
301.405.0146 (Fax)

HOME PAGE: http://www.rhsmith.umd.edu/marketing/faculty/wedel.html

University of Groningen - Faculty of Economics and Business ( email )

Postbus 72
9700 AB Groningen
Netherlands

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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