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Specification of mixed logit models assisted by an optimization framework

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  • Paz, Alexander
  • Arteaga, Cristian
  • Cobos, Carlos

Abstract

Mixed logit is a widely used discrete outcome model that requires for the analyst to make three important decisions that affect the quality of the model specification. These decisions are: 1) what variables are considered in the analysis, 2) which variables are to be modeled with random parameters, and 3) what density function do these parameters follow. The literature provides guidance; however, a strong statistical background and an ad hoc search process are required to obtain an adequate model specification. Knowledge and data about the problem context are required; also, the process is time consuming, and there is no certainty that the specified model is the best available. This paper proposes an algorithm to assist analysts in the search of an appropriate specification in terms of explanatory power and goodness of fit for mixed logit models. The specification includes the variables that should be considered as well as the random and deterministic parameters and their corresponding distributions. Three experiments were performed to test the effectiveness of the proposed algorithm. Comparison with existing model specifications for the same datasets were performed. The results suggest that the proposed algorithm can find adequate model specifications, thereby supporting the analyst in the modeling process.

Suggested Citation

  • Paz, Alexander & Arteaga, Cristian & Cobos, Carlos, 2019. "Specification of mixed logit models assisted by an optimization framework," Journal of choice modelling, Elsevier, vol. 30(C), pages 50-60.
    • Handle: RePEc:eee:eejocm:v:30:y:2019:i:c:p:50-60
    DOI: 10.1016/j.jocm.2019.01.001
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    References listed on IDEAS

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    4. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.
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    6. Faisal, Asif & Yigitcanlar, Tan & Paz, Alexander, 2023. "Understanding driverless car adoption: Random parameters ordered probit model for Brisbane, Melbourne and Sydney," Journal of Transport Geography, Elsevier, vol. 110(C).

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