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Generalized Robust Conjoint Estimation

Citations

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Cited by:

  1. Steven M. Shugan, 2006. "Editorial: Who Is Afraid to Give Freedom of Speech to Marketing Folks?," Marketing Science, INFORMS, vol. 25(5), pages 403-410, September.
  2. Daria Dzyabura & John R. Hauser, 2011. "Active Machine Learning for Consideration Heuristics," Marketing Science, INFORMS, vol. 30(5), pages 801-819, September.
  3. Julio López & Sebastián Maldonado & Ricardo Montoya, 2017. "Simultaneous preference estimation and heterogeneity control for choice-based conjoint via support vector machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1323-1334, November.
  4. Lee, Ungki & Kang, Namwoo & Lee, Ikjin, 2020. "Choice data generation using usage scenarios and discounted cash flow analysis," Journal of choice modelling, Elsevier, vol. 37(C).
  5. Dongling Huang & Lan Luo, 2016. "Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning," Marketing Science, INFORMS, vol. 35(3), pages 445-464, May.
  6. Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
  7. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
  8. John Hauser & Gerard J. Tellis & Abbie Griffin, 2006. "Research on Innovation: A Review and Agenda for," Marketing Science, INFORMS, vol. 25(6), pages 687-717, 11-12.
  9. Theodoros Evgeniou & Massimiliano Pontil & Olivier Toubia, 2007. "A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation," Marketing Science, INFORMS, vol. 26(6), pages 805-818, 11-12.
  10. Maldonado, Sebastián & Montoya, Ricardo & Weber, Richard, 2015. "Advanced conjoint analysis using feature selection via support vector machines," European Journal of Operational Research, Elsevier, vol. 241(2), pages 564-574.
  11. Yupeng Chen & Raghuram Iyengar & Garud Iyengar, 2017. "Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis—A Sparse Learning Approach," Marketing Science, INFORMS, vol. 36(1), pages 140-156, January.
  12. Vermeulen, Bart & Goos, Peter & Vandebroek, Martina, 2008. "Models and optimal designs for conjoint choice experiments including a no-choice option," International Journal of Research in Marketing, Elsevier, vol. 25(2), pages 94-103.
  13. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
  14. Michael Yee & Ely Dahan & John R. Hauser & James Orlin, 2007. "Greedoid-Based Noncompensatory Inference," Marketing Science, INFORMS, vol. 26(4), pages 532-549, 07-08.
  15. Lan Luo & P. K. Kannan & Brian T. Ratchford, 2007. "New Product Development Under Channel Acceptance," Marketing Science, INFORMS, vol. 26(2), pages 149-163, 03-04.
  16. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
  17. Steven M. Shugan, 2006. "Editorial: Errors in the Variables, Unobserved Heterogeneity, and Other Ways of Hiding Statistical Error," Marketing Science, INFORMS, vol. 25(3), pages 203-216, 05-06.
  18. Liu, Jiapeng & Liao, Xiuwu & Kadziński, Miłosz & Słowiński, Roman, 2019. "Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1071-1089.
  19. Díaz, Verónica & Montoya, Ricardo & Maldonado, Sebastián, 2023. "Preference estimation under bounded rationality: Identification of attribute non-attendance in stated-choice data using a support vector machines approach," European Journal of Operational Research, Elsevier, vol. 304(2), pages 797-812.
  20. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
  21. Karlson Pfannschmidt & Pritha Gupta & Bjorn Haddenhorst & Eyke Hullermeier, 2019. "Learning Context-Dependent Choice Functions," Papers 1901.10860, arXiv.org, revised Oct 2021.
  22. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
  23. Dhaoui, Abderrazak & Audi, Mohamed & Ouled Ahmed Ben Ali, Raja, 2015. "Revising empirical linkages between direction of Canadian stock price index movement and Oil supply and demand shocks: Artificial neural network and support vector machines approaches," MPRA Paper 66029, University Library of Munich, Germany.
  24. Alex Burnap & John Hauser, 2018. "Predicting "Design Gaps" in the Market: Deep Consumer Choice Models under Probabilistic Design Constraints," Papers 1812.11067, arXiv.org.
  25. Olivier Toubia & John Hauser & Rosanna Garcia, 2007. "Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application," Marketing Science, INFORMS, vol. 26(5), pages 596-610, 09-10.
  26. Halme, Merja & Kallio, Markku, 2011. "Estimation methods for choice-based conjoint analysis of consumer preferences," European Journal of Operational Research, Elsevier, vol. 214(1), pages 160-167, October.
  27. Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.
  28. Denis Sauré & Juan Pablo Vielma, 2019. "Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis," Operations Research, INFORMS, vol. 67(2), pages 315-338, March.
  29. Olivier Toubia & John R. Hauser, 2007. "—On Managerially Efficient Experimental Designs," Marketing Science, INFORMS, vol. 26(6), pages 851-858, 11-12.
  30. Ghaderi, Mohammad & Kadziński, Miłosz, 2021. "Incorporating uncovered structural patterns in value functions construction," Omega, Elsevier, vol. 99(C).
  31. 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.
  32. John R. Hauser & Felix Eggers & Matthew Selove, 2019. "The Strategic Implications of Scale in Choice-Based Conjoint Analysis," Marketing Science, INFORMS, vol. 38(6), pages 1059-1081, November.
  33. Olivier Toubia & Laurent Florès, 2007. "Adaptive Idea Screening Using Consumers," Marketing Science, INFORMS, vol. 26(3), pages 342-360, 05-06.
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