Economics at your fingertips  

Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling

Melvin Wong, Bilal Farooq and Guillaume-Alexandre Bilodeau

Journal of choice modelling, 2018, vol. 29, issue C, 152-168

Abstract: Conventional methods of estimating latent behaviour generally use attitudinal questions which are subjective and these survey questions may not always be available. We hypothesize that an alternative approach such as non-parametric artificial neural networks can be used for latent variable estimation through an undirected graphical models. In this study, we explore the use of generative non-parametric modelling methods to estimate latent variables from prior choice distribution without the conventional use of measurement indicators. A restricted Boltzmann machine is used to represent latent behaviour factors by analyzing the relationship information between the observed choices and explanatory variables. The algorithm is adapted for latent behaviour analysis in discrete choice scenario and we use a graphical approach to evaluate and understand the semantic meaning from estimated parameter vector values. We illustrate our methodology on a financial instrument choice dataset and perform statistical analysis on parameter sensitivity and stability. Our findings show that through non-parametric statistical tests, we can extract useful latent information on the behaviour of latent constructs through machine learning methods and present strong and significant influence on the choice process. Furthermore, our modelling framework shows robustness in input variability through sampling and validation.

Keywords: Machine learning; Latent behaviour models; Decision making; Financial instruments; Consumer behaviour (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed

Downloads: (external link)
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:

DOI: 10.1016/j.jocm.2017.11.003

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 ().

Page updated 2021-06-30
Handle: RePEc:eee:eejocm:v:29:y:2018:i:c:p:152-168