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Error Theory for Elimination by Aspects

Author

Listed:
  • Rajeev Kohli

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Kamel Jedidi

    (Graduate School of Business, Columbia University, New York, New York 10027)

Abstract

Elimination by aspects (EBA) is a random utility model that is considered to represent the choice process used by consumers more faithfully than logit and probit models. One limitation of the model is that it does not have a known error theory. We show that EBA can be derived by assuming that aspects have random utilities with independent, extreme value distributions. Multinomial logit and rank-ordered logit models are special cases of EBA.

Suggested Citation

  • Rajeev Kohli & Kamel Jedidi, 2015. "Error Theory for Elimination by Aspects," Operations Research, INFORMS, vol. 63(3), pages 512-526, June.
    • Handle: RePEc:inm:oropre:v:63:y:2015:i:3:p:512-526
    DOI: 10.1287/opre.2015.1373
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    References listed on IDEAS

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    1. Timothy J. Gilbride & Greg M. Allenby, 2006. "Estimating Heterogeneous EBA and Economic Screening Rule Choice Models," Marketing Science, INFORMS, vol. 25(5), pages 494-509, September.
    2. Beggs, S. & Cardell, S. & Hausman, J., 1981. "Assessing the potential demand for electric cars," Journal of Econometrics, Elsevier, vol. 17(1), pages 1-19, September.
    3. Rajeev Kohli & Kamel Jedidi, 2007. "Representation and Inference of Lexicographic Preference Models and Their Variants," Marketing Science, INFORMS, vol. 26(3), pages 380-399, 05-06.
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    Cited by:

    1. Anocha Aribarg & Thomas Otter & Daniel Zantedeschi & Greg M. Allenby & Taylor Bentley & David J. Curry & Marc Dotson & Ty Henderson & Elisabeth Honka & Rajeev Kohli & Kamel Jedidi & Stephan Seiler & X, 2018. "Advancing Non-compensatory Choice Models in Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 82-92, March.
    2. Rajeev Kohli & Kamel Jedidi, 2017. "Relation Between EBA and Nested Logit Models," Operations Research, INFORMS, vol. 65(3), pages 621-634, June.
    3. Rajeev Kohli & Khaled Boughanmi & Vikram Kohli, 2019. "Randomized Algorithms for Lexicographic Inference," Operations Research, INFORMS, vol. 67(2), pages 357-375, March.
    4. Dimitris Bertsimas & Velibor V. Mišić, 2019. "Exact First-Choice Product Line Optimization," Operations Research, INFORMS, vol. 67(3), pages 651-670, May.

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