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Flexible Covariance Structures for Categorical Dependent Variables through Finite Mixtures of Generalized Extreme Value Models

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  • Swait, Joffre

Abstract

A new class of finite mixture discrete choice models, denoted FinMix (fin miks), is introduced. These arise from the combination of a finite number of core Generalized Extreme Value (GEV) models to achieve more flexible functional forms, particularly in terms of error covariance structures. Example members of the class include combinations of (1) Multinomial Logit (MNL) models with differing scales, (2) multinomial logit with nested MNL models, (3) tree extreme value models with differing preference trees, and so on. Compatibility of FinMix models with utility maximization is easily determined, which permits empirical investigation of the suitability of specific model forms for economic evaluation exercises.

Suggested Citation

  • Swait, Joffre, 2003. "Flexible Covariance Structures for Categorical Dependent Variables through Finite Mixtures of Generalized Extreme Value Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 80-87, January.
    • Handle: RePEc:bes:jnlbes:v:21:y:2003:i:1:p:80-87
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    Citations

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    Cited by:

    1. Swait, Joffre, 2009. "Choice models based on mixed discrete/continuous PDFs," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 766-783, August.
    2. Wen, Chieh-Hua & Wang, Wei-Chung & Fu, Chiang, 2012. "Latent class nested logit model for analyzing high-speed rail access mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 545-554.
    3. Friederike Paetz & Winfried J. Steiner, 2017. "The benefits of incorporating utility dependencies in finite mixture probit models," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 793-819, July.
    4. 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.
    5. 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.
    6. Nurul Habib, Khandker & El-Assi, Wafic & Hasnine, Md. Sami & Lamers, James, 2017. "Daily activity-travel scheduling behaviour of non-workers in the National Capital Region (NCR) of Canada," Transportation Research Part A: Policy and Practice, Elsevier, vol. 97(C), pages 1-16.
    7. Lu, Zeng-Hua, 2009. "Covariate selection in mixture models with the censored response variable," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2710-2723, May.
    8. José Grisolía & Kenneth Willis, 2012. "A latent class model of theatre demand," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 36(2), pages 113-139, May.
    9. Swait, Joffre & Brigden, Neil & Johnson, Richard D., 2014. "Categories shape preferences: A model of taste heterogeneity arising from categorization of alternatives," Journal of choice modelling, Elsevier, vol. 13(C), pages 3-23.
    10. Newman, Jeffrey P., 2008. "Normalization of network generalized extreme value models," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 958-969, December.
    11. Papola, Andrea, 2016. "A new random utility model with flexible correlation pattern and closed-form covariance expression: The CoRUM," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 80-96.
    12. Sanjog Misra, 2005. "Generalized Reverse Discrete Choice Models," Quantitative Marketing and Economics (QME), Springer, vol. 3(2), pages 175-200, June.

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