Relative efficiency appraisal of discrete choice modeling algorithms using small-scale maximum likelihood estimator through empirically tailored computing environment View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-03

AUTHORS

Hyuk-Jae Roh, Prasanta K. Sahu, Ata M. Khan, Satish Sharma

ABSTRACT

Discrete choice models are widely used in multiple sectors such as transportation, health, energy, and marketing, etc., where the model estimation is usually carried out by using commercial software. Nonetheless, tailored computer codes offer modellers greater flexibility and control of unique modelling situation. Aligned with empirically tailored computing environment, this research discusses the relative performance of six different algorithms of a discrete choice model using three key performance measures: convergence time, number of iterations, and iteration time. The computer codes are developed by using Visual Basic Application (VBA). Maximum likelihood function (MLF) is formulated and the mathematical relationships of gradient and Hessian matrix are analytically derived to carry out the estimation process. The estimated parameter values clearly suggest that convergence criterion and initial guessing of parameters are the two critical factors in determining the overall estimation performance of a custom-built discrete choice model. More... »

PAGES

67-79

References to SciGraph publications

  • 2013-11. Enhancing algorithmic base for discrete choice modelling in KSCE JOURNAL OF CIVIL ENGINEERING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s40534-014-0063-x

    DOI

    http://dx.doi.org/10.1007/s40534-014-0063-x

    DIMENSIONS

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