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MM Algorithm for General Mixed Multinomial Logit Models

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  • Jonathan James

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  • Jonathan James, 2017. "MM Algorithm for General Mixed Multinomial Logit Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 841-857, June.
    • Handle: RePEc:wly:japmet:v:32:y:2017:i:4:p:841-857
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    Cited by:

    1. Esteban Aucejo & Jonathan James, 2021. "The Path to College Education: The Role of Math and Verbal Skills," Journal of Political Economy, University of Chicago Press, vol. 129(10), pages 2905-2946.
    2. Bansal, Prateek & Daziano, Ricardo A & Guerra, Erick, 2018. "Minorization-Maximization (MM) algorithms for semiparametric logit models: Bottlenecks, extensions, and comparisons," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 17-40.
    3. James Dawber & Nicola Salvati & Enrico Fabrizi & Nikos Tzavidis, 2022. "Expectile regression for multi‐category outcomes with application to small area estimation of labour force participation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 590-619, December.
    4. Jonathan James, 2018. "Estimation of Factor Structured Covariance Mixed Logit Models," Working Papers 1802, California Polytechnic State University, Department of Economics.
    5. Prateek Bansal & Daniel Hörcher & Daniel J. Graham, 2022. "A dynamic choice model to estimate the user cost of crowding with large‐scale transit data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 615-639, April.
    6. James, Jonathan, 2018. "Estimation of factor structured covariance mixed logit models," Journal of choice modelling, Elsevier, vol. 28(C), pages 41-55.
    7. Srikanth Jagabathula & Lakshminarayanan Subramanian & Ashwin Venkataraman, 2020. "A Conditional Gradient Approach for Nonparametric Estimation of Mixing Distributions," Management Science, INFORMS, vol. 66(8), pages 3635-3656, August.

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