Bayesian nonparametric mixed random utility models

George Karabatsos and Stephen G. Walker

Computational Statistics & Data Analysis, 2012, vol. 56, issue 6, 1714-1722

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.

Keywords: Mixed multinomial logit model; Stick-breaking priors; Bayesian nonparametrics (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:6:p:1714-1722

DOI: 10.1016/j.csda.2011.10.014

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