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

Man Suk Oh, Jung Whan Choi, Dai Gyoung Kim

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish
Pages (from-to)191-204
Number of pages14
JournalJournal of Applied Statistics
Volume30
Issue number2
DOIs
StatePublished - Feb 2003

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