The use of heuristic optimization algorithms to facilitate maximum simulated likelihood estimation of random parameter logit models

Hole, A.R. and Yoo, H.I. (2017) The use of heuristic optimization algorithms to facilitate maximum simulated likelihood estimation of random parameter logit models. Journal of the Royal Statistical Society. Series C: Applied Statistics, 66 (5). pp. 997-1013. ISSN 0035-9254

Abstract

Metadata

Authors/Creators:
  • Hole, A.R.
  • Yoo, H.I.
Copyright, Publisher and Additional Information: © 2017 The Authors Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Keywords: Mixed logit; generalized multinomial logit; differential evolution; particle swarm optimization
Dates:
  • Accepted: 28 November 2016
  • Published (online): 18 January 2017
  • Published: November 2017
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Economics (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 20 Dec 2016 13:27
Last Modified: 07 Nov 2018 10:11
Published Version: https://doi.org/10.1111/rssc.12209
Status: Published
Publisher: Wiley
Refereed: Yes
Identification Number: https://doi.org/10.1111/rssc.12209

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