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Bayesian nonparametric mixed random utility models

Author

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  • Karabatsos, George
  • Walker, Stephen G.

Abstract

We propose a mixed multinomial logit model, with the mixing distribution assigned a general (nonparametric) stick-breaking prior. We present a Markov chain Monte Carlo (MCMC) algorithm to sample and estimate the posterior distribution of the model’s parameters. The algorithm relies on a Gibbs (slice) sampler that is useful for Bayesian nonparametric (infinite-dimensional) models. The model and algorithm are illustrated through the analysis of real data involving 10 choice alternatives, and we prove the posterior consistency of the model.

Suggested Citation

  • Karabatsos, George & Walker, Stephen G., 2012. "Bayesian nonparametric mixed random utility models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1714-1722.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:1714-1722
    DOI: 10.1016/j.csda.2011.10.014
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    References listed on IDEAS

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    1. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2008. "A Bayesian mixed logit-probit model for multinomial choice," Journal of Econometrics, Elsevier, vol. 147(2), pages 232-246, December.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    3. Greg M. Allenby & Peter E. Rossi, 1991. "Quality Perceptions and Asymmetric Switching Between Brands," Marketing Science, INFORMS, vol. 10(3), pages 185-204.
    4. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    5. Stephen G. Walker & Antonio Lijoi & Igor Prunster, 2005. "Data tracking and the understanding of Bayesian consistency," Biometrika, Biometrika Trust, vol. 92(4), pages 765-778, December.
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