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Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis

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  • Alwosheel, Ahmad
  • van Cranenburgh, Sander
  • Chorus, Caspar G.

Abstract

Artificial Neural Networks (ANNs) are increasingly used for discrete choice analysis. But, at present, it is unknown what sample size requirements are appropriate when using ANNs in this particular context. This paper fills this knowledge gap: we empirically establish a rule-of-thumb for ANN-based discrete choice analysis based on analyses of synthetic and real data. To investigate the effect of complexity of the data generating process on the minimum required sample size, we conduct extensive Monte Carlo analyses using a series of different model specifications with different levels of model complexity, including RUM and RRM models, with and without random taste parameters. Based on our analyses we advise to use a minimum sample size of fifty times the number of weights in the ANN; it should be noted, that the number of weights is generally much larger than the number of parameters in a discrete choice model. This rule-of-thumb is considerably more conservative than the rule-of-thumb that is most often used in the ANN community, which advises to use at least ten times the number of weights.

Suggested Citation

  • Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 28(C), pages 167-182.
    • Handle: RePEc:eee:eejocm:v:28:y:2018:i:c:p:167-182
    DOI: 10.1016/j.jocm.2018.07.002
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    1. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457, Elsevier.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    3. Huber, Joel & Train, Kenneth, 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Department of Economics, Working Paper Series qt7zm4f51b, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    4. van Cranenburgh, Sander & Guevara, Cristian Angelo & Chorus, Caspar G., 2015. "New insights on random regret minimization models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 91-109.
    5. Davide Castelvecchi, 2016. "Can we open the black box of AI?," Nature, Nature, vol. 538(7623), pages 20-23, October.
    6. Caspar Chorus & Michel Bierlaire, 2013. "An empirical comparison of travel choice models that capture preferences for compromise alternatives," Transportation, Springer, vol. 40(3), pages 549-562, May.
    7. Hensher, David A. & Ton, Tu T., 2000. "A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 36(3), pages 155-172, September.
    8. John Rose & Michiel Bliemer, 2013. "Sample size requirements for stated choice experiments," Transportation, Springer, vol. 40(5), pages 1021-1041, September.
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