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Asymmetric, closed-form, finite-parameter models of multinomial choice

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  • Brathwaite, Timothy
  • Walker, Joan L.

Abstract

Class imbalance, where there are great differences between the number of observations associated with particular discrete outcomes, is common within transportation and other fields. In the statistics literature, one explanation for class imbalance that has been hypothesized is an asymmetric (rather than the typically symmetric) choice probability function. Unfortunately, few relatively simple models exist for testing this hypothesis in transportation settings—settings that are inherently multinomial. Our paper fills this gap.

Suggested Citation

  • Brathwaite, Timothy & Walker, Joan L., 2018. "Asymmetric, closed-form, finite-parameter models of multinomial choice," Journal of choice modelling, Elsevier, vol. 29(C), pages 78-112.
    • Handle: RePEc:eee:eejocm:v:29:y:2018:i:c:p:78-112
    DOI: 10.1016/j.jocm.2018.01.002
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    Cited by:

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    5. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.
    6. Tinessa, Fiore, 2021. "Closed-form random utility models with mixture distributions of random utilities: Exploring finite mixtures of qGEV models," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 262-288.
    7. Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
    8. Stephen D. Wong & Jacquelyn C. Broader & Joan L. Walker & Susan A. Shaheen, 2023. "Understanding California wildfire evacuee behavior and joint choice making," Transportation, Springer, vol. 50(4), pages 1165-1211, August.
    9. Wong, Stephen D & Pel, Adam J & Shaheen, Susan A & Chorus, Caspar G, 2020. "Fleeing from Hurricane Irma: Empirical Analysis of Evacuation Behavior Using Discrete Choice Theory," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt02f296df, Institute of Transportation Studies, UC Berkeley.
    10. Tomhave, Benjamin J. & Khani, Alireza, 2022. "Refined choice set generation and the investigation of multi-criteria transit route choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 484-500.
    11. Li, Dawei & Feng, Siqi & Song, Yuchen & Lai, Xinjun & Bekhor, Shlomo, 2023. "Asymmetric closed-form route choice models: Formulations and comparative applications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    12. Francisco C. Pereira, 2019. "Rethinking travel behavior modeling representations through embeddings," Papers 1909.00154, arXiv.org.
    13. 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.
    14. Gu, Yu & Chen, Anthony & Kitthamkesorn, Songyot, 2022. "Weibit choice models: Properties, mode choice application and graphical illustrations," Journal of choice modelling, Elsevier, vol. 44(C).
    15. Rico Krueger & Michel Bierlaire & Thomas Gasos & Prateek Bansal, 2020. "Robust discrete choice models with t-distributed kernel errors," Papers 2009.06383, arXiv.org, revised Dec 2022.
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    17. Subodh Dubey & Prateek Bansal & Ricardo A. Daziano & Erick Guerra, 2019. "A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel," Papers 1904.08332, arXiv.org, revised Jan 2020.

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