Rico Krueger, Akshay Vij, Taha H. Rashidi
arXiv 19 Jan 2018 · Statistics — Applications · publishedJournal of Choice Modelling (2020) · 14 citations (OpenAlex)
arXiv:1801.06296 · PDF · DOI · OpenAlex · Extracted main text
We present a mixed multinomial logit (MNL) model, which leverages the truncated stick-breaking process representation of the Dirichlet process as a flexible nonparametric mixing distribution. The proposed model is a Dirichlet process mixture model and accommodates discrete representations of heterogeneity, like a latent class MNL model. Yet, unlike a latent class MNL model, the proposed discrete choice model does not require the analyst to fix the number of mixture components prior to estimation, as the complexity of the discrete mixing distribution is inferred from the evidence. For posterior inference in the proposed Dirichlet process mixture model of discrete choice, we derive an expectation maximisation algorithm. In a simulation study, we demonstrate that the proposed model framework can flexibly capture differently-shaped taste parameter distributions. Furthermore, we empirically validate the model framework in a case study on motorists' route choice preferences and find that the proposed Dirichlet process mixture model of discrete choice outperforms a latent class MNL model and mixed MNL models with common parametric mixing distributions in terms of both in-sample fit and out-of-sample predictive ability. Compared to extant modelling approaches, the proposed discrete choice model substantially abbreviates specification searches, as it relies on less restrictive parametric assumptions and does not require the analyst to specify the complexity of the discrete mixing distribution prior to estimation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Train, K. E (2008) EM Algorithms for nonparametric estimation of mixing distributions | 1.000 | 13 | 5 | 100% |
| 2 | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A.,… (2013) Bayesian Data Analysis, Third Edition | 1.000 | 11 | 3 | 100% |
| 3 | Vij, A. and Krueger, R (2017) Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions self | 1.000 | 9 | 3 | 100% |
| 4 | Train, K. E (2009) Discrete Choice Methods with Simulation | 1.000 | 7 | 3 | 100% |
| 5 | Bhat, C. R (1997) An Endogenous Segmentation Mode Choice Model with an Application to Intercity Travel | 1.000 | 5 | 3 | 100% |
| 6 | Ishwaran, H. and James, L. F (2001) Gibbs Sampling Methods for Stick-Breaking Priors | 1.000 | 5 | 3 | 100% |
| 7 | Li, Y. and Ansari, A (2013) A Bayesian Semiparametric Approach for Endogeneity and Heterogeneity in Choice Models | 1.000 | 5 | 3 | 100% |
| 8 | Teh, Y. W (2011) Dirichlet Process | 0.874 | 7 | 2 | 100% |
| 9 | Ferguson, T. S (1973) A Bayesian Analysis of Some Nonparametric Problems | 0.874 | 6 | 2 | 100% |
| 10 | Blei, D. M. and Jordan, M. I (2006) Variational inference for Dirichlet process mixtures | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 69 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Sparse Covariance Estimation in Logit Mixture Models | 0.405 | 1 | 1 |