Latent class choice model with a flexible class membership component: A mixture model approach

Georges Sfeir, Maya Abou-Zeid, Filipe Rodrigues, Francisco Camara Pereira and Isam Kaysi

Journal of choice modelling, 2021, vol. 41, issue C

Abstract: This study presents a Latent Class Choice Model (LCCM) with a flexible class membership component. Specifically, it formulates the latent classes using Gaussian-Bernoulli mixture models and investigates the impact of such formulation on the representation of heterogeneity in the choice process, goodness-of-fit measures and out-of-sample prediction accuracy of the choice models. Mixture models are model-based clustering techniques that have been widely used in areas such as machine learning, data mining and pattern recognition for clustering and classification problems. An Expectation-Maximization (EM) algorithm is derived for the estimation of the proposed model. Using two different case studies on travel mode choice behavior, the proposed model is compared to traditional discrete choice models on the basis of parameter estimates’ signs, values of time, statistical goodness-of-fit measures, and cross-validation tests. Results show that mixture models improve the overall performance of latent class choice models by providing better out-of-sample predication accuracy in addition to better representations of heterogeneity without weakening the behavioral and economic interpretability of the choice models.

Keywords: Econometric models; Discrete choice models; Latent class choice models; Machine learning; Mixture models (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1755534521000531
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:41:y:2021:i:c:s1755534521000531

DOI: 10.1016/j.jocm.2021.100320

Access Statistics for this article

Journal of choice modelling is currently edited by S. Hess and J.M. Rose

More articles in Journal of choice modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().