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Best, worst, and best&worst choice probabilities for logit and reverse logit models

Author

Listed:
  • André de Palma
  • Karim Kilani

    (Université de Cergy-Pontoise, THEMA)

Abstract

This paper builds upon the work of Professor Marley, who, since the beginning of his long research career, has proposed rigorous axiomatics in the area of probabilistic choice models. Our study concentrates on models that can be applied to best and worst choice scaling experiments. We focus on those among these models that are based on strong assumptions about the underlying ranking of the alternatives with which the individual is assumed to be endowed when making the choice. Taking advantage of an inclusion-exclusion identity that we showed a few years ago, we propose a variety of best-worst choice probability models that could be implemented in software packages that are flourishing in this field.

Suggested Citation

  • André de Palma & Karim Kilani, 2023. "Best, worst, and best&worst choice probabilities for logit and reverse logit models," THEMA Working Papers 2023-16, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    • Handle: RePEc:ema:worpap:2023-16
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    References listed on IDEAS

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    1. Delle Site, Paolo & Kilani, Karim & Gatta, Valerio & Marcucci, Edoardo & de Palma, André, 2019. "Estimation of consistent Logit and Probit models using best, worst and best–worst choices," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 87-106.
    2. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    3. Anderson, Simon P. & de Palma, Andre, 1999. "Reverse discrete choice models," Regional Science and Urban Economics, Elsevier, vol. 29(6), pages 745-764, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Best-worst scaling experiments; Logit model; Random utility models; Reverse logit model;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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