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An adaptive Monte Carlo algorithm for computing mixed logit estimators

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  • Fabian Bastin
  • Cinzia Cirillo
  • Philippe Toint

Abstract

Researchers and analysts are increasingly using mixed logit models for estimating responses to forecast demand and to determine the factors that affect individual choices. However the numerical cost associated to their evaluation can be prohibitive, the inherent probability choices being represented by multidimensional integrals. This cost remains high even if Monte Carlo or quasi-Monte Carlo techniques are used to estimate those integrals. This paper describes a new algorithm that uses Monte Carlo approximations in the context of modern trust-region techniques, but also exploits accuracy and bias estimators to considerably increase its computational efficiency. Numerical experiments underline the importance of the choice of an appropriate optimisation technique and indicate that the proposed algorithm allows substantial gains in time while delivering more information to the practitioner. Copyright Springer-Verlag Berlin/Heidelberg 2006

Suggested Citation

  • Fabian Bastin & Cinzia Cirillo & Philippe Toint, 2006. "An adaptive Monte Carlo algorithm for computing mixed logit estimators," Computational Management Science, Springer, vol. 3(1), pages 55-79, January.
    • Handle: RePEc:spr:comgts:v:3:y:2006:i:1:p:55-79
    DOI: 10.1007/s10287-005-0044-y
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    Citations

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    Cited by:

    1. Johannes Royset, 2013. "On sample size control in sample average approximations for solving smooth stochastic programs," Computational Optimization and Applications, Springer, vol. 55(2), pages 265-309, June.
    2. Kuo-Hao Chang & L. Jeff Hong & Hong Wan, 2013. "Stochastic Trust-Region Response-Surface Method (STRONG)---A New Response-Surface Framework for Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 230-243, May.
    3. Carolina Osorio & Michel Bierlaire, 2013. "A Simulation-Based Optimization Framework for Urban Transportation Problems," Operations Research, INFORMS, vol. 61(6), pages 1333-1345, December.
    4. Stefania Bellavia & Nataša Krejić & Benedetta Morini, 2020. "Inexact restoration with subsampled trust-region methods for finite-sum minimization," Computational Optimization and Applications, Springer, vol. 76(3), pages 701-736, July.
    5. Wang, Xinchang, 2016. "Optimal allocation of limited and random network resources to discrete stochastic demands for standardized cargo transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 310-331.
    6. Emelogu, Adindu & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Bian, Linkan & Eksioglu, Burak, 2016. "An enhanced sample average approximation method for stochastic optimization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 230-252.
    7. Munger, D. & L’Ecuyer, P. & Bastin, F. & Cirillo, C. & Tuffin, B., 2012. "Estimation of the mixed logit likelihood function by randomized quasi-Monte Carlo," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 305-320.
    8. Piergiacomo Sabino, 2011. "Implementing quasi-Monte Carlo simulations with linear transformations," Computational Management Science, Springer, vol. 8(1), pages 51-74, April.
    9. Nataša Krejić & Nataša Krklec Jerinkić, 2019. "Spectral projected gradient method for stochastic optimization," Journal of Global Optimization, Springer, vol. 73(1), pages 59-81, January.
    10. Jeffrey Larson & Stephen C. Billups, 2016. "Stochastic derivative-free optimization using a trust region framework," Computational Optimization and Applications, Springer, vol. 64(3), pages 619-645, July.
    11. Johannes O. Royset & Roberto Szechtman, 2013. "Optimal Budget Allocation for Sample Average Approximation," Operations Research, INFORMS, vol. 61(3), pages 762-776, June.

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