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

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  • Man-Suk Oh
  • Jung Whan Choi
  • Dai-Gyoung Kim

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.

Suggested Citation

  • Man-Suk Oh & Jung Whan Choi & Dai-Gyoung Kim, 2003. "Bayesian inference and model selection in latent class logit models with parameter constraints: An application to market segmentation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(2), pages 191-204.
    • Handle: RePEc:taf:japsta:v:30:y:2003:i:2:p:191-204
    DOI: 10.1080/0266476022000023749
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    Cited by:

    1. Tsai, Ming-Chih & Yang, Chih-Wen & Lee, Hsiao-Ching & Lien, Ching-Wei, 2011. "Segmenting industrial competitive markets: An example from air freight," Journal of Air Transport Management, Elsevier, vol. 17(4), pages 211-214.
    2. Matthew Nagler, 2006. "An exploratory analysis of the determinants of cooperative advertising participation rates," Marketing Letters, Springer, vol. 17(2), pages 91-102, April.
    3. Joyee Ghosh & Amy H. Herring & Anna Maria Siega-Riz, 2011. "Bayesian Variable Selection for Latent Class Models," Biometrics, The International Biometric Society, vol. 67(3), pages 917-925, September.
    4. Kevin Dayaratna & Jesse Crosson & Chandler Hubbard, 2022. "Closed Form Bayesian Inferences for Binary Logistic Regression with Applications to American Voter Turnout," Stats, MDPI, vol. 5(4), pages 1-21, November.

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