A Latent Class Poisson Regression Model for Heterogeneous Count Data

Journal of Applied Econometrics, Vol. 8, No. 4, pp. 397-411

Posted: 6 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

Venkatram Ramaswamy

University of Michigan, Stephen M. Ross School of Business

Date Written: December 1993

Abstract

In this paper an approach is developed that accommodates heterogeneity in Poisson regression models for count data. The model developed assumes that heterogeneity arises from a distribution of both the intercept and the coefficients of the explanatory variables. We assume that the mixing distribution is discrete, resulting in a finite mixture model formulation. An EM algorithm for estimation is described, and the algorithm is applied to data on customer purchases of books offered through direct mail. Our model is compared empirically to a number of other approaches that deal with heterogeneity in Poisson regression models.

Suggested Citation

Wedel, Michel and DeSarbo, Wayne S. and Ramaswamy, Venkatram, A Latent Class Poisson Regression Model for Heterogeneous Count Data (December 1993). Journal of Applied Econometrics, Vol. 8, No. 4, pp. 397-411. Available at SSRN: https://ssrn.com/abstract=2789639

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

Venkatram Ramaswamy

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

701 Tappan Street
Ann Arbor, MI MI 48109-1234
United States
734-763-5932 (Phone)
734-936-0279 (Fax)

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