Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity

International Journal of Research in Marketing - Tập 28 - Trang 378-388 - 2011
Jie Yu1, Peter Goos2,3, Martina Vandebroek1,4
1Faculty of Business and Economics, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium
2Faculty of Applied Economics & StatUa Center for Statistics, University of Antwerp, Prinsstraat 13, B-2000 Antwerpen, Belgium
3Erasmus School of Economics, Erasmus University Rotterdam, Postbus 1738, 3000 DR Rotterdam, The Netherlands
4Leuven Statistics Research Centre, KULeuven, Celestijnenlaan 200B, B-3001 Leuven, Belgium

Tài liệu tham khảo

Allenby, 1999, Marketing models of consumer heterogeneity, Journal of Econometrics, 89, 57, 10.1016/S0304-4076(98)00055-4 Arora, 1998, A hierarchical Bayes model of primary and secondary demand, Marketing Science, 17, 29, 10.1287/mksc.17.1.29 Arora, 2001, Improving parameter estimates and model prediction by aggregate customization in choice experiments, Journal of Consumer Research, 28, 273, 10.1086/322902 Bedrick, 1997, Binomial regression: Predicting survival at a trauma center, The American Statistician, 51, 211, 10.2307/2684890 Bliemer, 2010, Construction of experimental designs for mixed logit models allowing for correlation across choice observations, Transportation Research Part B, 44, 720, 10.1016/j.trb.2009.12.004 Gelman, 1995 Hensher, 2003, The mixed logit model: The state of practice, Transportation, 30, 133, 10.1023/A:1022558715350 Kessels, 2006, A comparison of criteria to design efficient choice experiments, Journal of Marketing Research, 43, 409, 10.1509/jmkr.43.3.409 Kessels, 2009, An efficient algorithm for constructing Bayesian optimal choice designs, Journal of Business and Economic Statistics, 27, 279, 10.1198/jbes.2009.0026 Lenk, 1996, Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs, Marketing Science, 15, 173, 10.1287/mksc.15.2.173 Meyer, 1995, The coordinate-exchange algorithm for constructing exact optimal experimental designs, Technometrics, 37, 60, 10.2307/1269153 Monahan, 1997, Spherical-radial integration rules for Bayesian computation, Journal of the American Statistical Association, 92, 664, 10.2307/2965714 Pilz, 1991 Rossi, 2005 Sándor, 2004, Alternative sampling methods for estimating multivariate normal probabilities, Journal of Econometrics, 2, 207, 10.1016/S0304-4076(03)00212-4 Sándor, 2001, Designing conjoint choice experiments using managers' prior beliefs, Journal of Marketing Research, 38, 430, 10.1509/jmkr.38.4.430.18904 Sándor, 2002, Profile construction in experimental choice designs for mixed logit models, Marketing Science, 21, 455, 10.1287/mksc.21.4.455.131 Sándor, 2005, Heterogeneous conjoint choice designs, Journal of Marketing Research, 42, 210, 10.1509/jmkr.42.2.210.62285 Sawtooth Software Inc. Toubia, 2004, Polyhedral methods for adaptive choice-based conjoint analysis, Journal of Marketing Research, 41, 116, 10.1509/jmkr.41.1.116.25082 Toubia, 2007, Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application, Marketing Science, 26, 596, 10.1287/mksc.1060.0257 Train, 2003 Yu, 2008, A comparison of different Bayesian design criteria for constructing efficient conjoint choice experiments Yu, 2009, Efficient conjoint choice designs in the presence of respondent heterogeneity, Marketing Science, 28, 122, 10.1287/mksc.1080.0386 Yu, 2010, Comparing different sampling schemes for approximating the integrals involved in the efficient design of stated choice experiments, Transportation Research Part B, 44, 1268, 10.1016/j.trb.2010.02.005