Bayesian inference and model selection in latent class logit models with parameter constraints: An application to market segmentation

Man-Suk Oh, Jung Whan Choi and Dai-Gyoung Kim

Journal of Applied Statistics, 2003, vol. 30, issue 2, 191-204

Abstract: Latent class models have recently drawn considerable attention among many researchers and practitioners as a class of useful tools for capturing heterogeneity across different segments in a target market or population. In this paper, we consider a latent class logit model with parameter constraints and deal with two important issues in the latent class models--parameter estimation and selection of an appropriate number of classes--within a Bayesian framework. A simple Gibbs sampling algorithm is proposed for sample generation from the posterior distribution of unknown parameters. Using the Gibbs output, we propose a method for determining an appropriate number of the latent classes. A real-world marketing example as an application for market segmentation is provided to illustrate the proposed method.

Date: 2003
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DOI: 10.1080/0266476022000023749

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