Bayesian inference and model selection in latent class logit models with parameter constraints: an application to market segmentation |
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Authors: | Man-Suk Oh Jung Whan Choi Dai-Gyoung Kim |
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Affiliation: | a Department of Statistics, Ewha University, Seoul, Korea.b ERM Strategy, Seurat Consulting, Chicago, USA.c Department of Mathematics, Hanyang University, Ansan, Kyung-Ki 425-791, Korea. |
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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. |
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