A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation

Published Online:https://doi.org/10.1287/mksc.1070.0291

References

  • Allenby G. M., Rossi P. E. Marketing models of consumer heterogeneity. J. Econometrics (1999) 89(March/April):57–78Google Scholar
  • Ando R. K., Zhang T. A framework for learning predictive structures from multiple tasks and unlabeled data. J. Machine Learn. Res. (2005) 6:1817–1853Google Scholar
  • Arora N., Huber J. Improving parameter estimates and model prediction by aggregate customization in choice experiments. J. Consumer Res. (2001) 28(SeptemberGoogle Scholar
  • Baxter J. A Bayesian/information theoretic model of learning to learn via multiple task sampling. Machine Learn. (1997) 28:7–39CrossrefGoogle Scholar
  • Boyd S., Vandenberghe L.Convex Optimization (2004) (Cambridge University Press, Oxford, UK) CrossrefGoogle Scholar
  • Bradlow E., Hu Y., Ho T.-H. A learning-based model for imputing missing levels in partial conjoint profiles. J. Marketing Res. (2004) 41(4):369–381CrossrefGoogle Scholar
  • Bunch D. S., Louviere J. J., Anderson D. A comparison of experimental design strategies for multinominal logit models: The case of generic attributes. (1994) . Working paper, Graduate School of Management, University of California at DavisGoogle Scholar
  • Carson R. T., Louviere J. J., Anderson D. A., Arabie P., Bunch D. S., Hensher D. A., Johnson R. M., Kuhfeld W. F., Steinberg D., Swait J., Timmermans H., Wiley J. B. Experimental analysis of choice. Marketing Lett. (1994) 5(4):351–367CrossrefGoogle Scholar
  • Caruana R. Multi-task learning. Machine Learn. (1997) 28:41–75CrossrefGoogle Scholar
  • Chaloner K., Verdinelli I. Bayesian experimental design: A review. Statistical Sci. (1995) 10(3):273–304CrossrefGoogle Scholar
  • Cucker F., Smale S. On the mathematical foundations of learning. Bull. Amer. Math. Soc. (2002) 39(1):1–49CrossrefGoogle Scholar
  • Cui D., Curry D. Prediction in marketing using the support vector machine. Marketing Sci. (2005) 24(4):595–615LinkGoogle Scholar
  • Efron B., Tibshirani R.An Introduction to the Bootstrap (1993) (Chapman and Hall, New York) CrossrefGoogle Scholar
  • Evgeniou T., Boussios C., Zacharia G. Generalized robust conjoint estimation. Marketing Sci. (2005) 24(3):415–429LinkGoogle Scholar
  • Evgeniou T., Micchelli C., Pontil M. Learning multiple tasks with kernel methods. J. Machine Learn. Res. (2005b) 6:615–637Google Scholar
  • Fisher R. A. On the mathematical foundations of theoretical statistics. Phil. Trans. Roy. Soc., Ser. A (1922) 222–326Google Scholar
  • Gilbride T., Allenby G. M. A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Sci. (2004) 23(3):391–406LinkGoogle Scholar
  • Gilbride T., Allenby G. M. Estimating heterogenous EBA and economic screening rule choice models. Marketing Sci. (2006) 25(5):494–509LinkGoogle Scholar
  • Girosi F., Jones M., Poggio T. Regularization theory and neural networks architectures. Neural Comput. (1995) 7:219–269CrossrefGoogle Scholar
  • Hastie T., Tibshirani R., Friedman J. H.The Elements of Statistical Learning (2003) Springer Series in StatisticsGoogle Scholar
  • Hauser J., Toubia O. The impact of utility balance and endogeneity in conjoint analysis. Marketing Sci. (2005) 24(3):498–507LinkGoogle Scholar
  • Hauser J., Tellis G. J., Griffin A. Research on innovation: A review and agenda for marketing science. Marketing Sci. (2005) 25(6):687–717LinkGoogle Scholar
  • Huber J., Zwerina K. The importance of utility balance in efficient choice designs. J. Marketing Res. (1996) 32(August):308–317Google Scholar
  • Jebara T. Multi-task feature and kernel selection for SVMs. Proc. Twenty-First Internat. Conf. Machine Learning (2004) Banff, Alberta, Canada:55–63CrossrefGoogle Scholar
  • Jaakkola T., Haussler D. Probabilistic kernel regression models. Proc. Seventh Internat. Workshop on Artificial Intelligence and Statist (1999) (Morgan Kaufmann, San Francisco, CA) Google Scholar
  • Jedidi K., Kohli R. Probabilistic subset-conjunctive models for heterogenous consumers. J. Marketing Res. (2005) 42:483–494CrossrefGoogle Scholar
  • Keerthi S., Duan K., Shevade S., Poo A. A fast dual algorithm for kernel logistic regression. Machine Learn. (2005) 61(1–3, November):151–165CrossrefGoogle Scholar
  • Lenk P. J., DeSarbo W. S., Green P. E., Young M. R. Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. (1996) 15(2):173–91LinkGoogle Scholar
  • Liechty J. C., Fong D. K. H., DeSarbo W. S. Dynamic models incorporating individual heterogeneity: Utility evolution in conjoint analysis. Marketing Sci. (2005) 24(2):285–293LinkGoogle Scholar
  • Liu Q., Otter T., Allenby G. M. Investigating endogeneity bias in marketing. Marketing Sci. (2007) 26(5):642–650LinkGoogle Scholar
  • Louviere J. J., Hensher D. A., Swait J. D.Stated Choice Methods: Analysis and Applications (2000) (Cambridge University Press, New York) CrossrefGoogle Scholar
  • Micchelli C., Pontil M. On learning vector–valued functions. Neural Comput. (2005) 17:177–204CrossrefGoogle Scholar
  • Mika S., Schölkopf B., Smola A. J., Müller K.-R., Scholz M., Rätsch G., Kearns M. S., Solla S. A., Cohn D. A. Kernel PCA and de-noising in feature spaces. Advances in Neural Information Processing Systems (1999) 11(MIT Press, Cambridge, MA) 536–542Google Scholar
  • Minka T. A comparison of numerical optimizers for logistic regression. (2003) . Microsoft research tech reportGoogle Scholar
  • Rossi P. E., Allenby G. M. A Bayesian approach to estimating household parameters. J. Marketing Res. (1993) 30(2):171–182CrossrefGoogle Scholar
  • Rossi P. E., Allenby G. M. Bayesian statistics and marketing. Marketing Sci. (2003) 22(3):304–328LinkGoogle Scholar
  • Rossi P. E., Allenby G. M., McCulloch R.Bayesian Statistics and Marketing (2005) (John Wiley and Sons, New York) CrossrefGoogle Scholar
  • Shao J. Linear model selection via cross-validation. J. Amer. Statist. Assoc. (1993) 88(422):486–494CrossrefGoogle Scholar
  • Srinivasan V., Aronson J. E., Zionts S. A strict paired comparison linear programming approach to nonmetric conjoint analysis. Operations Research: Methods, Models and Applications (1998) (Quorum Books, Westport, CT) 97–111Google Scholar
  • Srinivasan V., Shocker A. D. Linear programming techniques for multidimensional analysis of preferences. Psychometrica (1973) 38(3):337–369CrossrefGoogle Scholar
  • Thrun S., Pratt L.Learning to Learn (1997) (Kluwer Academic Publishers, Dordrecht, The Netherlands) Google Scholar
  • Tikhonov A. N., Arsenin V. Y.Solutions of Ill-Posed Problems (1977) (W. H. Winston, Washington, D.C.) Google Scholar
  • Toubia O., Hauser J. R., Simester D. I. Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. (2004) 46(February):116–131CrossrefGoogle Scholar
  • Toubia O., Simester D. I., Hauser J. R., Dahan E. Fast polyhedral adaptive conjoint estimation. Marketing Sci. (2003) 22(3):273–303LinkGoogle Scholar
  • Vapnik V.Statistical Learning Theory (1998) (Wiley, New York) Google Scholar
  • Wahba G.Splines Models for Observational Data, Vol. 59. Series in Applied Mathematics (1990) (SIAM, Philadelphia, PA) CrossrefGoogle Scholar
  • Zhu J., Hastie T. Kernel logistic regression and the import vector machine. J. Computational Graphical Statist. (2005) 14(1):185–205CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.