An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs
Abstract
Recently, Kessels et al. (2006) developed a way to produce Bayesian G- and V-optimal designs for the multinomial logitmodel. These designs allow for precise response predictions which is the goal of conjoint choice experiments. The authors showed that the G- and V- optimality criteria outperform the D- and A-optimality criteria for prediction. However, their G- and V-optimal design algorithm is computationally intensive, which is a barrier to its use in practice. In this paper, we present an efficient algorithm for calculating Bayesian optimal designs by means of the different criteria. Particularly, the speed of computation for the V-optimality criterion has improved dramatically.The new algorithm makes it possible to use Bayesian D-, A-, G- and V-optimal designs that are tailored to individual respondents in computerized conjoint choice studies.(This abstract was borrowed from another version of this item.)
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Bibliographic Info
Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 27 (2009)
Issue (Month): 2 ()
Pages: 279-291
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Related research
Keywords:Other versions of this item:
- Kessels, R & Jones, B & Goos, Peter & Vandebroek, Martina, 2006. "An efficient algorithm for constructing Bayesian optimal choice designs," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/228661, Katholieke Universiteit Leuven.
References
References listed on IDEASPlease report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
- Sandor, Zsolt & Andras, P.Peter, 2004. "Alternative sampling methods for estimating multivariate normal probabilities," Journal of Econometrics, Elsevier, vol. 120(2), pages 207-234, June.
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- Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- 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.
- Kessels R. & Bradley J. & Goos P., 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.
- Yu, Jie & Goos, Peter & Vandebroek, Martina, 2007.
"Efficient conjoint choice designs in the presence of respondent heterogeneity,"
Open Access publications from Katholieke Universiteit Leuven
urn:hdl:123456789/175480, Katholieke Universiteit Leuven.
- 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.
- Vermeulen, Bart & Goos, Peter & Scarpa, Riccardo & Vandebroek, Martina, 2010.
"Bayesian conjoint choice designs for measuring willingness to pay,"
Open Access publications from Katholieke Universiteit Leuven
urn:hdl:123456789/272171, Katholieke Universiteit Leuven.
- Bart Vermeulen & Peter Goos & Riccardo Scarpa & Martina Vandebroek, 2011. "Bayesian Conjoint Choice Designs for Measuring Willingness to Pay," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 48(1), pages 129-149, January.
- 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.
- Yu, Jie & Goos, Peter & Vandebroek, Martina, 2010. "Comparing different sampling schemes for approximating the integrals involved in the efficient design of stated choice experiments," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/260768, Katholieke Universiteit Leuven.
- Goos P. & Vermeulen B. & Vandebroek M., 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.
- Yu, Jie & Goos, Peter & Vandebroek, Martina, 2008. "Comparing different sampling schemes for approximating the integrals involved in the semi-Bayesian optimal design of choice experiments," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/205410, Katholieke Universiteit Leuven.
- Vermeulen, Bart & Goos, Peter & Vandebroek, Martina, 2010.
"Obtaining more information from conjoint experiments by best-worst choices,"
Open Access publications from Katholieke Universiteit Leuven
urn:hdl:123456789/255632, Katholieke Universiteit Leuven.
- 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.
- Yu, Jie & Goos, Peter & Vandebroek, Martina, 2008. "A comparison of different Bayesian design criteria to compute efficient conjoint choice experiments," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/196127, Katholieke Universiteit Leuven.
- J. DeShazo & Trudy Cameron & Manrique Saenz, 2009. "The Effect of Consumers’ Real-World Choice Sets on Inferences from Stated Preference Surveys," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 42(3), pages 319-343, March.
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