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An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs

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  1. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2011. "Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 378-388.
  2. Aiste Ruseckaite & Peter Goos & Dennis Fok, 2017. "Bayesian D-optimal choice designs for mixtures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 363-386, February.
  3. Chang Wang & Dries Goossens & Martina Vandebroek, 2018. "The Impact of the Soccer Schedule on TV Viewership and Stadium Attendance," Journal of Sports Economics, , vol. 19(1), pages 82-112, January.
  4. Vermeulen, Bart & Goos, Peter & Vandebroek, Martina, 2010. "Obtaining more information from conjoint experiments by best-worst choices," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1426-1433, June.
  5. Richard G. Newell & Juha Siikamäki, 2014. "Nudging Energy Efficiency Behavior: The Role of Information Labels," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 1(4), pages 555-598.
  6. Verhetsel, Ann & Kessels, Roselinde & Goos, Peter & Zijlstra, Toon & Blomme, Nele & Cant, Jeroen, 2015. "Location of logistics companies: a stated preference study to disentangle the impact of accessibility," Journal of Transport Geography, Elsevier, vol. 42(C), pages 110-121.
  7. Qing Liu & Yihui (Elina) Tang, 2015. "Construction of Heterogeneous Conjoint Choice Designs: A New Approach," Marketing Science, INFORMS, vol. 34(3), pages 346-366, May.
  8. Robert J. Johnston & Kevin J. Boyle & Wiktor (Vic) Adamowicz & Jeff Bennett & Roy Brouwer & Trudy Ann Cameron & W. Michael Hanemann & Nick Hanley & Mandy Ryan & Riccardo Scarpa & Roger Tourangeau & Ch, 2017. "Contemporary Guidance for Stated Preference Studies," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(2), pages 319-405.
  9. Palhazi Cuervo, Daniel & Kessels, Roselinde & Goos, Peter & Sörensen, Kenneth, 2016. "An integrated algorithm for the optimal design of stated choice experiments with partial profiles," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 648-669.
  10. Andreas Falke & Harald Hruschka, 2017. "A Monte Carlo study of design-generating algorithms for the latent class mixed logit model," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 1035-1053, October.
  11. Bart Vermeulen & Peter Goos & Riccardo Scarpa & Martina Vandebroek, 2011. "Bayesian Conjoint Choice Designs for Measuring Willingness to Pay," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(1), pages 129-149, January.
  12. Kessels, Roselinde & Jones, Bradley & Goos, Peter, 2019. "Using Firth's method for model estimation and market segmentation based on choice data," Journal of choice modelling, Elsevier, vol. 31(C), pages 1-21.
  13. Bliemer, Michiel C.J. & Rose, John M., 2010. "Construction of experimental designs for mixed logit models allowing for correlation across choice observations," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 720-734, July.
  14. John Rose & Michiel Bliemer, 2013. "Sample size requirements for stated choice experiments," Transportation, Springer, vol. 40(5), pages 1021-1041, September.
  15. Qing Liu & Neeraj Arora, 2011. "Efficient Choice Designs for a Consider-Then-Choose Model," Marketing Science, INFORMS, vol. 30(2), pages 321-338, 03-04.
  16. John M. Rose & Michiel C.J. Bliemer, 2014. "Stated choice experimental design theory: the who, the what and the why," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 7, pages 152-177, Edward Elgar Publishing.
  17. Sándor Zsolt, 2013. "Monte Carlo Simulation in Random Coefficient Logit Models Involving Large Sums," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 1(1), pages 85-108, July.
  18. KESSELS, Roselinde & BRADLEY, Jones & GOOS, Peter, 2012. "A comparison of partial profile designs for discrete choice experiments with an application in software development," Working Papers 2012004, University of Antwerp, Faculty of Business and Economics.
  19. GOOS, Peter & VERMEULEN, Bart & VANDEBROEK, Martina, 2008. "D-optimal conjoint choice designs with no-choice options for a nested logit model," Working Papers 2008020, University of Antwerp, Faculty of Business and Economics.
  20. Rakhi Singh & Angela Dean & Ashish Das & Fangfang Sun, 2021. "A-optimal designs under a linearized model for discrete choice experiments," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(4), pages 445-465, May.
  21. Nedka Dechkova Nikiforova & Rossella Berni & Jesús Fernando López‐Fidalgo, 2022. "Optimal approximate choice designs for a two‐step coffee choice, taste and choice again experiment," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1895-1917, November.
  22. Andreas Falke & Harald Hruschka, 2017. "Setting prices in mixed logit model designs," Marketing Letters, Springer, vol. 28(1), pages 139-154, March.
  23. Frischknecht, Bart D. & Eckert, Christine & Geweke, John & Louviere, Jordan J., 2014. "A simple method for estimating preference parameters for individuals," International Journal of Research in Marketing, Elsevier, vol. 31(1), pages 35-48.
  24. Falke Andreas & Hruschka Harald, 2016. "A Monte Carlo Study of Design Procedures for the Semi-parametric Mixed Logit Model," Review of Marketing Science, De Gruyter, vol. 14(1), pages 21-67, June.
  25. KESSELS, Roselinde & JONES, Bradley & GOOS, Peter, 2013. "An argument for preferring Firth bias-adjusted estimates in aggregate and individual-level discrete choice modeling," Working Papers 2013013, University of Antwerp, Faculty of Business and Economics.
  26. Jie Yu & Peter Goos & Martina Vandebroek, 2009. "Efficient Conjoint Choice Designs in the Presence of Respondent Heterogeneity," Marketing Science, INFORMS, vol. 28(1), pages 122-135, 01-02.
  27. Sanko, Nobuhiro & Yamamoto, Toshiyuki, 2013. "Estimation efficiency of RP/SP models considering SP design and error structures," Journal of choice modelling, Elsevier, vol. 6(C), pages 60-73.
  28. Dellaert, Benedict G.C. & Arentze, Theo & Horeni, Oliver & Timmermans, Harry J.P., 2017. "Deriving attribute utilities from mental representations of complex decisions," Journal of choice modelling, Elsevier, vol. 22(C), pages 24-38.
  29. Denis Sauré & Juan Pablo Vielma, 2019. "Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis," Operations Research, INFORMS, vol. 67(2), pages 315-338, March.
  30. J. DeShazo & Trudy Cameron & Manrique Saenz, 2009. "The Effect of Consumers’ Real-World Choice Sets on Inferences from Stated Preference Surveys," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 42(3), pages 319-343, March.
  31. Víctor Casero-Alonso & Jesús López-Fidalgo, 2015. "Experimental designs in triangular simultaneous equations models," Statistical Papers, Springer, vol. 56(2), pages 273-290, May.
  32. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2010. "Comparing different sampling schemes for approximating the integrals involved in the efficient design of stated choice experiments," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1268-1289, December.
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