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An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs

Author

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  • Kessels, Roselinde
  • Jones, Bradley
  • Goos, Peter
  • Vandebroek, Martina

Abstract

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Suggested Citation

  • Kessels, Roselinde & Jones, Bradley & Goos, Peter & Vandebroek, Martina, 2009. "An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 279-291.
  • Handle: RePEc:bes:jnlbes:v:27:i:2:y:2009:p:279-291
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    Citations

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    Cited by:

    1. John M. Rose & Michiel C.J. Bliemer, 2014. "Stated choice experimental design theory: the who, the what and the why," Chapters,in: Handbook of Choice Modelling, chapter 7, pages 152-177 Edward Elgar Publishing.
    2. 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.
    3. repec:eee:ijrema:v:28:y:2011:i:4:p:378-388 is not listed on IDEAS
    4. 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 Applied Economics.
    5. 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.
    6. 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.
    7. repec:bla:jorssc:v:66:y:2017:i:2:p:363-386 is not listed on IDEAS
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Sándor Zsolt, 2013. "Monte Carlo Simulation in Random Coefficient Logit Models Involving Large Sums," Acta Universitatis Sapientiae, Economics and Business, De Gruyter Open, vol. 1(1), pages 85-108, July.
    13. 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 Applied Economics.
    14. 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 Applied Economics.
    15. 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.
    16. 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.
    17. 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.
    18. Andreas Falke & Harald Hruschka, 2017. "Setting prices in mixed logit model designs," Marketing Letters, Springer, vol. 28(1), pages 139-154, March.
    19. John Rose & Michiel Bliemer, 2013. "Sample size requirements for stated choice experiments," Transportation, Springer, vol. 40(5), pages 1021-1041, September.
    20. 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.
    21. Víctor Casero-Alonso & Jesús López-Fidalgo, 2015. "Experimental designs in triangular simultaneous equations models," Statistical Papers, Springer, vol. 56(2), pages 273-290, May.
    22. repec:eee:ijrema:v:31:y:2014:i:1:p:35-48 is not listed on IDEAS
    23. 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.
    24. VERHETSEL, Ann & KESSELS, Roselinde & BLOMME, Nele & CANT, Jeroen & GOOS, Peter, 2013. "Location of logistics companies: A stated preference study to disentangle the impact of accessibility," Working Papers 2013024, University of Antwerp, Faculty of Applied Economics.
    25. repec:spr:orspec:v:39:y:2017:i:4:d:10.1007_s00291-017-0483-1 is not listed on IDEAS

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