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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.

Abstract

Applications of random-parameter logit models can be found in various disciplines. These models have non-concave simulated likelihood functions and the choice of starting values is therefore crucial to avoid convergence at an inferior optimum. Little guidance exists, however, on how to obtain good starting values. We apply an estimation strategy which makes joint use of heuristic global search routines and gradient-based algorithms. The central idea is to use heuristic routines to locate a starting point which is likely to be close to the global maximum, and then to use gradient-based algorithms to refine this point further. Using four empirical data sets, as well as simulated data, we find that the strategy proposed locates higher maxima than more conventional estimation strategies.

Item Type:Article
Full text:(AM) Accepted Manuscript
First Live Deposit - 29 November 2016
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1111/rssc.12209
Publisher statement:© 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, 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.
Record Created:29 Nov 2016 13:59
Last Modified:06 Oct 2017 15:52

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