Bayesian Inference for Finite Mixtures of Generalized Linear Models with Random Effects

Posted: 13 Jun 2016

See all articles by Peter Lenk

Peter Lenk

University of Michigan, Stephen M. Ross School of Business

Wayne S. DeSarbo

Pennsylvania State University

Date Written: March 2000

Abstract

We present an hierarchical Bayes approach to modeling parameter heterogeneity in generalized linear models. The model assumes that there are relevant subpopulations and that within each subpopulation the individual-level regression coefficients have a multivariate normal distribution. However, class membership is not known a priori, so the heterogeneity in the regression coefficients becomes a finite mixture of normal distributions. This approach combines the flexibility of semiparametric, latent class models that assume common parameters for each sub-population and the parsimony of random effects models that assume normal distributions for the regression parameters. The number of subpopulations is selected to maximize the posterior probability of the model being true. Simulations are presented which document the performance of the methodology for synthetic data with known heterogeneity and number of sub-populations. An application is presented concerning preferences for various aspects of personal computers.

Keywords: Bayesian inference, consumer behavior, finite mixtures, generalized linear models, heterogeneity, latent class analysis, Markov chain Monte Carlo

Suggested Citation

Lenk, Peter and DeSarbo, Wayne S., Bayesian Inference for Finite Mixtures of Generalized Linear Models with Random Effects (March 2000). Available at SSRN: https://ssrn.com/abstract=2792372

Peter Lenk

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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