1. Incentive alignment in anchored MaxDiff yields superior predictive validity. (2025). Lichters, Marcel ; Schramm, Joshua Benjamin.
   In: Marketing Letters.
   RePEc:kap:mktlet:v:36:y:2025:i:1:d:10.1007_s11002-023-09714-2.

   Full description at Econpapers || Download paper

2. Greening the telecommunications industry – Consumer preferences and surcharges for environmental attributes of mobile phone plans. (2025). Mazurek, Jessica ; Prey, Raul.
   In: Telecommunications Policy.
   RePEc:eee:telpol:v:49:y:2025:i:5:s0308596125000539.

   Full description at Econpapers || Download paper

3. Rental clothing box subscription: The importance of sustainable fashion labels. (2025). Rese, Alexandra ; Baier, Daniel.
   In: Journal of Retailing and Consumer Services.
   RePEc:eee:joreco:v:83:y:2025:i:c:s0969698924004491.

   Full description at Econpapers || Download paper

4. Predicting and optimizing marketing performance in dynamic markets. (2024). Paetz, Friederike ; Wagner, Udo ; Wedel, Michel ; Guhl, Daniel.
   In: OR Spectrum: Quantitative Approaches in Management.
   RePEc:spr:orspec:v:46:y:2024:i:1:d:10.1007_s00291-024-00755-1.

   Full description at Econpapers || Download paper

5. Substitution Patterns and Price Response for Plant-Based Meat Alternatives. (2024). Jahn, Steffen ; Guhl, Daniel ; Erhard, Ainslee.
   In: Rationality and Competition Discussion Paper Series.
   RePEc:rco:dpaper:509.

   Full description at Econpapers || Download paper

1. Akinc, D., & Vandebroek, M. (2018). Bayesian estimation of mixed logit models: Selecting an appropriate prior for the covariance matrix. Journal of Choice Modelling, 29, 133–151. https://doi.org/10.1016/j.jocm.2017.11.004 .

2. Allenby, G. M., & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392–403.
   Paper not yet in RePEc: Add citation now
   

3. Allenby, G. M., Brazell, J. D., Howell, J. R., & Rossi, P. E. (2014). Economic valuation of product features. Quantitative Marketing and Economics, 12, 421–456. https://doi.org/10.1007/s11129-014-9150-x .

4. Arndt, A. D., Ford, J. B., Babin, B. J., & Luong, V. (2022). Collecting samples from online services: How to use screeners to improve data quality. International Journal of Research in Marketing, 39, 117–133. https://doi.org/10.1016/j.ijresmar.2021.05.001 .

5. Bacon, L., & Lenk, P. (2012). Augmenting discrete-choice data to identify common preference scales for inter-subject analyses. Quantitative Marketing and Economics, 10, 453–474. https://doi.org/10.1007/s11129-012-9124-9 .

6. Bauer, R., Menrad, K., & Decker, T. (2015). Adaptive hybrid methods for choice-based conjoint analysis: A comparative study. International Journal of Marketing Studies, 7, 1–14. https://doi.org/10.5539/ijms.v7n1p1 .
   Paper not yet in RePEc: Add citation now
   
7. Becker, G. M., Degroot, M. H., & Marschak, J. (1964). Measuring utility by a single-response sequential method. Behavioral Science, 9, 226–232. https://doi.org/10.1002/bs.3830090304 .
   Paper not yet in RePEc: Add citation now
   
8. Befurt, R., MacMenamin, N., & Mohammad, A. P. (2023). Use of Conjoint Analysis in Litigation. In J. E. Gersen & J. H. Steckel (Eds.), The Cambridge Handbook of Marketing and the Law (pp. 221–235). Cambridge University Press.
   Paper not yet in RePEc: Add citation now
   
9. Bijmolt, T. H., Van Heerde, H. J., & Pieters, R. G. (2005). New empirical generalizations on the determinants of price elasticity. Journal of Marketing Research, 42, 141–156. https://doi.org/10.1509/jmkr.42.2.141.62296 .
   Paper not yet in RePEc: Add citation now
   

10. Brucks, M. (1985). The effects of product class knowledge on information search behavior. Journal of Consumer Research, 12, 1–16. https://doi.org/10.1086/209031 .

11. Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., et al. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1), 1–32.

12. de Groot, I. B., Otten, W., Dijs-Elsinga, J., Smeets, H. J., Kievit, J., & Marang-van de Mheen, P. J. (2012). Choosing between hospitals: The influence of the experiences of other patients. Medical Decision Making, 32, 764–778. https://doi.org/10.1177/0272989X12443416 .

13. Derpanopoulos, G., Overmann, J., & Wazzan, C. P. (2022). The use of conjoint analysis in high-stakes litigation: A historical review up to Navarro et. al., v. Procter and Gamble, which withstood a rigorous Daubert challenge. Journal of the Patent and Trademark Office Society, 102(3), 502–526.
    Paper not yet in RePEc: Add citation now
    
14. Ding, M. (2007). An incentive-aligned mechanism for conjoint analysis. Journal of Marketing Research, 44, 214–223. https://doi.org/10.1509/jmkr.44.2.214 .
    Paper not yet in RePEc: Add citation now
    

15. Ding, M., Grewal, R., & Liechty, J. C. (2005). Incentive-aligned conjoint analysis. Journal of Marketing Research, 42, 67–82. https://doi.org/10.1509/jmkr.42.1.67.56890 .

16. Ding, M., Hauser, J. R., Dong, S., Dzyabura, D., Yang, Z., Su, C., et al. (2011). Unstructured direct elicitation of decision rules. Journal of Marketing Research, 48, 116–127. https://doi.org/10.1509/jmkr.48.1.116 .
    Paper not yet in RePEc: Add citation now
    

17. Ding, M., Park, Y.-H., & Bradlow, E. T. (2009). Barter markets for conjoint analysis. Management Science, 55, 1003–1017. https://doi.org/10.1287/mnsc.1090.1003 .

18. Dong, S., Ding, M., & Huber, J. (2010). A simple mechanism to incentive-align conjoint experiments. International Journal of Research in Marketing, 27, 25–32. https://doi.org/10.1016/j.ijresmar.2009.09.004 .

19. Dotson, J. P., Howell, J. R., Brazell, J. D., Otter, T., Lenk, P. J., MacEachern, S., et al. (2018). A probit model with structured covariance for similarity effects and source of volume calculations. Journal of Marketing Research, 55, 35–47. https://doi.org/10.1509/jmr.13.0240 .
    Paper not yet in RePEc: Add citation now
    

20. Dowling, K., Guhl, D., Klapper, D., Spann, M., Stich, L., & Yegoryan, N. (2020). Behavioral biases in marketing. Journal of the Academy of Marketing Science, 48, 449–477. https://doi.org/10.1007/s11747-019-00699-x .

21. Eggers, F., & Sattler, H. (2009). Hybrid individualized two-level choice-based conjoint (HIT-CBC): A new method for measuring preference structures with many attribute levels. International Journal of Research in Marketing, 26, 108–118. https://doi.org/10.1016/j.ijresmar.2009.01.002 .

22. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014). Bayestian data analysis (3rd ed.). Chapman & Hall/CRC Boca Raton.
    Paper not yet in RePEc: Add citation now
    

23. Gensler, S., Hinz, O., Skiera, B., & Theysohn, S. (2012). Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs. European Journal of Operational Research, 219, 368–378. https://doi.org/10.1016/j.ejor.2012.01.002 .

24. Gilbride, T. J., & Allenby, G. M. (2004). A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Science, 23, 391–406. https://doi.org/10.1287/mksc.1030.0032 .

25. Green, P. E., & Krieger, A. M. (1988). Choice rules and sensitivity analysis in conjoint simulators. Journal of the Academy of Marketing Science, 16, 114–127. https://doi.org/10.1007/BF02723330 .
    Paper not yet in RePEc: Add citation now
    

26. Guo, L. (2022). Testing the role of contextual deliberation in the compromise effect. Management Science, 68, 3975–4753. https://doi.org/10.1287/mnsc.2021.4038 .

27. Hauser, J. R., Eggers, F., & Selove, M. (2019). The strategic implications of scale in choice-based conjoint analysis. Marketing Science, 38, 913–1084. https://doi.org/10.1287/mksc.2019.1178 .

28. Huang, D., & Luo, L. (2016). Consumer preference elicitation of complex products using fuzzy support vector machine active learning. Marketing Science, 35, 445–464. https://doi.org/10.1287/mksc.2015.0946 .

29. Huber, J., & Zwerina, K. (1996). The importance of utility balance in efficient choice designs. Journal of Marketing Research, 33(3), 307–317.
    Paper not yet in RePEc: Add citation now
    
30. Johnson, R. M., & Orme, B. K. (2007). A new approach to adaptive CBC. Sawtooth Software Inc. http://www.sawtoothsoftware.com/support/technical-papers/adaptive-cbc-papers/a-new-approach-to-adaptive-cbc-2007 . Accessed 18 June 2014.
    Paper not yet in RePEc: Add citation now
    

31. Joo, M., Thompson, M. L., & Allenby, G. M. (2019). Optimal product design by sequential experiments in high dimensions. Management Science, 65, 3235–3254. https://doi.org/10.1287/mnsc.2018.3088 .

32. Kamakura, W. A., & Russell, G. J. (1989). A probabilistic choice model for market segmentation and elasticity structure. Journal of Marketing Research, 26, 379–390. https://doi.org/10.1177/002224378902600401 .
    Paper not yet in RePEc: Add citation now
    

33. Keller, K., Schlereth, C., & Hinz, O. (2021). Sample-based longitudinal discrete choice experiments: Preferences for electric vehicles over time. Journal of the Academy of Marketing Science, 49, 482–500. https://doi.org/10.1007/s11747-020-00758-8 .

34. Kouki-Block, M., & Wellbrock, C.-M. (2021). Influenced by media brands? A conjoint experiment on the effect of media brands on online media planners’ decision-making. Journal of Media Business Studies, 1–23. https://doi.org/10.1080/16522354.2021.1899741 .
    Paper not yet in RePEc: Add citation now
    

35. Kübler, R. V., Langmaack, M., Albers, S., & Hoyer, W. D. (2020). The impact of value-related crises on price and product-performance elasticities. Journal of the Academy of Marketing Science, 48, 776–794. https://doi.org/10.1007/s11747-019-00702-5 .

36. Kuhfeld, W. F., Tobias, R. D., & Garratt, M. (1994). Efficient experimental design with marketing research applications. Journal of Marketing Research, 31(4), 545–557.
    Paper not yet in RePEc: Add citation now
    
37. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174. https://doi.org/10.2307/2529310 .
    Paper not yet in RePEc: Add citation now
    

38. Lenk, P. J., Desarbo, W. S., Green, P. E., & Young, M. R. (1996). Hierarchical bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Science, 15, 173–191. https://doi.org/10.1287/mksc.15.2.173 .

39. Li, Y., Krefeld-Schwalb, A., Wall, D. G., Johnson, E. J., Toubia, O., & Bartels, D. M. (2022). The more you ask, the less you get: When additional questions hurt external validity. Journal of Marketing Research, 59, 963–982. https://doi.org/10.1177/00222437211073581 .
    Paper not yet in RePEc: Add citation now
    

40. Lichters, M., Bengart, P., Sarstedt, M., & Vogt, B. (2017). What really matters in attraction effect research: When choices have economic consequences. Marketing Letters, 28, 127–138. https://doi.org/10.1007/s11002-015-9394-6 .

41. Lichters, M., Wackershauser, V., Han, S., & Vogt, B. (2019). On the applicability of the BDM mechanism in product evaluation. Journal of Retailing and Consumer Services, 51, 1–7. https://doi.org/10.1016/j.jretconser.2019.02.021 .

42. Liu, Q., & Tang, Y. (2015). Construction of heterogeneous conjoint choice designs: A new approach. Marketing Science, 34, 346–366. https://doi.org/10.1287/mksc.2014.0897 .

43. Louviere, J. J., & Woodworth, G. (1983). Design and analysis of simulated consumer choice or allocation experiments: An approach based on aggregate data. Journal of Marketing Research, 20(4), 350–367.
    Paper not yet in RePEc: Add citation now
    

44. Lusk, J. L., Fields, D., & Prevatt, W. (2008). An incentive compatible conjoint ranking mechanism. American Journal of Agricultural Economics, 90, 487–498. https://doi.org/10.1111/j.1467-8276.2007.01119.x .

45. McLean, K. G., Hanson, D. J., Jervis, S. M., & Drake, M. A. (2017). Consumer perception of retail pork bacon attributes using adaptive choice-based conjoint analysis and maximum differential scaling. Journal of Food Science, 82, 2659–2668. https://doi.org/10.1111/1750-3841.13934 .
    Paper not yet in RePEc: Add citation now
    
46. McShane, B. B., & Böckenholt, U. (2017). Single paper meta-analysis: Benefits for study summary, theory-testing, and replicability. Journal of Consumer Research, 43, 1048–1063. https://doi.org/10.1093/jcr/ucw085 .
    Paper not yet in RePEc: Add citation now
    
47. Meißner, M., Musalem, A., & Huber, J. (2016). Eye-tracking reveals processes that enable conjoint choices to become increasingly efficient with practice. Journal of Marketing Research, 53, 1–17. https://doi.org/10.1509/jmr.13.0467 .
    Paper not yet in RePEc: Add citation now
    

48. Meyer, J., Shankar, V., & Berry, L. L. (2018). Pricing hybrid bundles by understanding the drivers of willingness to pay. Journal of the Academy of Marketing Science, 46, 497–515. https://doi.org/10.1007/s11747-017-0546-5 .

49. Miller, K. M., Hofstetter, R., Krohmer, H., & Zhang, Z. J. (2011). How should consumers’ willingness to pay be measured? An empirical comparison of state-of-the-art approaches. Journal of Marketing Research, 48, 172–184. https://doi.org/10.1509/jmkr.48.1.172 .
    Paper not yet in RePEc: Add citation now
    
50. Morrin, M., & Ratneshwar, S. (2003). Does it make sense to use scents to enhance brand memory? Journal of Marketing Research, 40, 10–25. https://doi.org/10.1509/jmkr.40.1.10.19128 .
    Paper not yet in RePEc: Add citation now
    
51. Orme, B. K. (2020). Getting started with conjoint analysis: Strategies for product design and pricing research (4th ed.). Research Publishers LLC.
    Paper not yet in RePEc: Add citation now
    
52. Orme, B. K., & Chrzan, K. (2017). Becoming an expert in conjoint analysis: Choice modeling for pros. Sawtooth Software.
    Paper not yet in RePEc: Add citation now
    
53. Orme, B. K., & Heft, M. (1999). Predicting actual sales with CBC: How capturing heterogeneity improves results (pp. 183–199). Sawtooth Software, Inc.
    Paper not yet in RePEc: Add citation now
    

54. Pachali, M. J., Kurz, P., & Otter, T. (2023). Omitted budget constraint bias and implications for competitive pricing. Journal of Marketing Research, 60, 968–986. https://doi.org/10.1177/00222437221145283 .

55. Papies, D., Eggers, F., & Wlömert, N. (2011). Music for free? How free ad-funded downloads affect consumer choice. Journal of the Academy of Marketing Science, 39, 777–794. https://doi.org/10.1007/s11747-010-0230-5 .
    Paper not yet in RePEc: Add citation now
    

56. Rao, A. (2015). Online content pricing: Purchase and rental markets. Marketing Science, 34, 430–451. https://doi.org/10.1287/mksc.2014.0896 .

57. Rao, V. R. (2014). Applied conjoint analysis. Springer.

58. Sawtooth Software Inc. (2022a). Lighthouse Studio 9: Software for web interviewing and conjoint analysis. Sawtooth Software Inc.
    Paper not yet in RePEc: Add citation now
    
59. Sawtooth Software Inc. (2023). Lighthouse Studio: Software for web interviewing and conjoint analysis. Sawtooth Software Inc.
    Paper not yet in RePEc: Add citation now
    

60. Schlereth, C., & Skiera, B. (2017). Two new features in discrete choice experiments to improve willingness-to-pay estimation that result in SDR and SADR: Separated (adaptive) dual response. Management Science, 63, 829–842. https://doi.org/10.1287/mnsc.2015.2367 .

61. Schmidt, J., & Bijmolt, T. H. A. (2020). Accurately measuring willingness to pay for consumer goods: A meta-analysis of the hypothetical bias. Journal of the Academy of Marketing Science, 48, 499–518. https://doi.org/10.1007/s11747-019-00666-6 .

62. Searle, S. R., Speed, F. M., & Milliken, G. A. (1980). Population marginal means in the linear model: An alternative to least squares means. The American Statistician, 34, 216. https://doi.org/10.2307/2684063 .
    Paper not yet in RePEc: Add citation now
    
63. Sipos, P., & Voeth, M. (2015). Performance, motivation and ability – testing a pay-for-performance incentive mechanism for conjoint analysis. In Sawtooth Software (Ed.), Proceedings of the Sawtooth Software Conference. Orlando (pp. 143–157). Sawtooth Software Inc.
    Paper not yet in RePEc: Add citation now
    

64. Toubia, O., de Jong, M. G., Stieger, D., & Füller, J. (2012). Measuring consumer preferences using conjoint poker. Marketing Science, 31, 138–156. https://doi.org/10.1287/mksc.1110.0672 .

65. Toubia, O., Hauser, J. R., & Simester, D. I. (2004). Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research, 41, 116–131. https://doi.org/10.1509/jmkr.41.1.116.25082 .
    Paper not yet in RePEc: Add citation now
    

66. Toubia, O., Simester, D. I., Hauser, J. R., & Dahan, E. (2003). Fast polyhedral adaptive conjoint estimation. Marketing Science, 22, 273–303. https://doi.org/10.1287/mksc.22.3.273.17743 .

67. Vadali, S. (2016). The performance of competitive and lottery incentive schemes vis-a-vis fixed fee incentive schemes in improving conjoint analysis. International Journal of Management and Marketing Research, 9(2), 81–100.

68. Vohs, K. D., Baumeister, R. F., Schmeichel, B. J., Twenge, J. M., Nelson, N. M., & Tice, D. M. (2014). Making choices impairs subsequent self-control: A limited-resource account of decision making, self-regulation, and active initiative. Motivation Science, 1, 19–42. https://doi.org/10.1037/2333-8113.1.S.19 .
    Paper not yet in RePEc: Add citation now
    

69. Voleti, S., Srinivasan, V., & Ghosh, P. (2017). An approach to improve the predictive power of choice-based conjoint analysis. International Journal of Research in Marketing, 34, 325–335. https://doi.org/10.1016/j.ijresmar.2016.08.007 .

70. Wertenbroch, K., & Skiera, B. (2002). Measuring consumers’ willingness to pay at the point of purchase. Journal of Marketing Research, 39, 228–241. https://doi.org/10.1509/jmkr.39.2.228.19086 .
    Paper not yet in RePEc: Add citation now
    
71. Wlömert, N., & Eggers, F. (2016). Predicting new service adoption with conjoint analysis: External validity of BDM-based incentive-aligned and dual-response choice designs. Marketing Letters, 27, 195–210. https://doi.org/10.1007/s11002-014-9326-x .
    Paper not yet in RePEc: Add citation now
    
72. Wuebker, R., Hampl, N., & Wüstenhagen, R. (2015). The strength of strong ties in an emerging industry: Experimental evidence of the effects of status hierarchies and personal ties in venture capitalist decision making. Strategic Entrepreneurship Journal, 9, 167–187. https://doi.org/10.1002/sej.1188 .
    Paper not yet in RePEc: Add citation now
    
73. Yang, L., Toubia, O., & de Jong, M. G. (2015). A bounded rationality model of information search and choice in preference measurement. Journal of Marketing Research, 52, 166–183. https://doi.org/10.1509/jmr.13.0288 .
    Paper not yet in RePEc: Add citation now
    
74. Yang, L., Toubia, O., & de Jong, M. G. (2018). Attention, information processing, and choice in incentive-aligned choice experiments. Journal of Marketing Research, 55, 783–800. https://doi.org/10.1177/0022243718817004 .
    Paper not yet in RePEc: Add citation now
    

75. Yu, J., Goos, P., & Vandebroek, M. (2011). Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity. International Journal of Research in Marketing, 28, 378–388. https://doi.org/10.1016/j.ijresmar.2011.06.002 .

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13. Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity. (2021). Rashidi, Taha H ; Krueger, Rico ; Daziano, Ricardo A ; Bansal, Prateek ; Bierlaire, Michel.
    In: Journal of choice modelling.
    RePEc:eee:eejocm:v:41:y:2021:i:c:s1755534521000567.

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14. Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations. (2020). Rashidi, Taha H ; Krueger, Rico ; Daziano, Ricardo A ; Bansal, Prateek ; Bierlaire, Michel.
    In: Transportation Research Part B: Methodological.
    RePEc:eee:transb:v:131:y:2020:i:c:p:124-142.

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15. A Dirichlet process mixture model of discrete choice: Comparisons and a case study on preferences for shared automated vehicles. (2020). Rashidi, Taha H ; Krueger, Rico ; Vij, Akshay.
    In: Journal of choice modelling.
    RePEc:eee:eejocm:v:36:y:2020:i:c:s1755534520300282.

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16. Logit mixture with inter and intra-consumer heterogeneity and flexible mixing distributions. (2020). Ben-Akiva, Moshe ; Danaf, Mazen ; Atasoy, Bilge.
    In: Journal of choice modelling.
    RePEc:eee:eejocm:v:35:y:2020:i:c:s1755534519300934.

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17. Endogeneity in adaptive choice contexts: Choice-based recommender systems and adaptive stated preferences surveys. (2020). Guevara, Cristian ; Ben-Akiva, Moshe ; Danaf, Mazen ; Atasoy, Bilge.
    In: Journal of choice modelling.
    RePEc:eee:eejocm:v:34:y:2020:i:c:s1755534519301058.

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18. Sparse Covariance Estimation in Logit Mixture Models. (2020). Aboutaleb, Youssef M ; Xie, Yifei ; Ben-Akiva, Moshe ; Danaf, Mazen.
    In: Papers.
    RePEc:arx:papers:2001.05034.

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19. Variational Bayesian Inference for Mixed Logit Models with Unobserved Inter- and Intra-Individual Heterogeneity. (2020). Rashidi, Taha H ; Krueger, Rico ; Daziano, Ricardo A ; Bansal, Prateek ; Bierlaire, Michel.
    In: Papers.
    RePEc:arx:papers:1905.00419.

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20. Reminder Nudge, Attribute Nonattendance, and Willingness to Pay in a Discrete Choice Experiment. (2020). Scarpa, Riccardo ; Kassie, Girma ; Birhanu, Mulugeta ; Zeleke, Fresenbet.
    In: 2020 Annual Meeting, July 26-28, Kansas City, Missouri.
    RePEc:ags:aaea20:304208.

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21. On the effect of HB covariance matrix prior settings: A simulation study. (2019). Steiner, Winfried J ; Kurz, Peter ; Hein, Maren.
    In: Journal of choice modelling.
    RePEc:eee:eejocm:v:31:y:2019:i:c:p:51-72.

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22. Semi-Parametric Hierarchical Bayes Estimates of New Yorkers Willingness to Pay for Features of Shared Automated Vehicle Services. (2019). Rashidi, Taha H ; Krueger, Rico ; Vij, Akshay.
    In: Papers.
    RePEc:arx:papers:1907.09639.

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23. P\olygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models. (2019). Rashidi, Taha H ; Krueger, Rico ; Daziano, Ricardo A ; Bansal, Prateek ; Bierlaire, Michel.
    In: Papers.
    RePEc:arx:papers:1904.07688.

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24. Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations. (2019). Rashidi, Taha H ; Krueger, Rico ; Daziano, Ricardo A ; Bansal, Prateek ; Bierlaire, Michel.
    In: Papers.
    RePEc:arx:papers:1904.03647.

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