Generalized Robust Conjoint Estimation

References

• Allenby Greg M., Rossi Peter E. Marketing models of consumer heterogeneity. J. Econometrics (1999) 89(March/April):57–78Google Scholar
• Allenby Greg M., Arora Neeraj, Ginter James L. On the heterogeneity of demand. J. Marketing Res. (1998) 35:384–389Crossref, Google Scholar
• Alon Noga, Ben-David Shai, Cesa-Bianchi Nicolò, Haussler David. Scale-sensitive dimensions, uniform convergence, and learnability. 34th IEEE Sympos. Foundations Comput. Sci. (1993) October 1993Palo Alto, CACrossref, Google Scholar
• Andrews Rick, Ansari Asim, Currim Imran. Hierarchical Bayes versus finite mixture conjoint analysis models: a comparison of fit, prediction, and partworth recovery. J. Marketing Res. (2002) 39:87–98Crossref, Google Scholar
• Arora Neeraj, Huber Joel. Improving parameter estimates and model prediction by aggregate customization in choice experiments. J. Consumer Res. (2001) 28:273–283Crossref, Google Scholar
• Arora Neeraj, Allenby Greg, Ginter James. A hierarchical Bayes model of primary and secondary demand. Marketing Sci. (1998) 17(1):29–44Link, Google Scholar
• Ben-Akiva Moshe, Boccara Bruno. Discrete choice models with latent choice sets. Internat. J. Res. Marketing (1995) 12:9–24Crossref, Google Scholar
• Ben-Akiva Moshe, Lerman Steven R.Discrete Choice Analysis: Theory and Application to Travel Demand (1985) (MIT Press, Cambridge, MA) Google Scholar
• Ben-Akiva Moshe, McFadden Daniel, Abe Makoto, Bockenholt Ulf, Bolduc Denis, Gopinath Dinesh, Morikawa Takayuki, Ramaswamy Venkatra, Rao Vithala, Revelt David, Steinberg Dan. Modeling methods for discrete choice analysis. Marketing Lett. (1997) 8(3):273–286Crossref, Google Scholar
• Bennett Kristin, Bredensteiner Erin, Langley Pat. Duality and geometry in SVM classifiers. Proc. Seventeenth Internat. Conf. Machine Learning (2000) (Morgan Kaufmann, San Francisco, CA) 57–64Google Scholar
• Bennett Kristin, Momma Michinari, Embrechts J. MARK: a boosting algorithm for heterogeneous kernel models. Proc. SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (2002) Edmonton, Alberta, Canada(ACM, New York) Crossref, Google Scholar
• Bertsimas Dimitris, Tsitsikilis John. Introduction to Linear Optimization (1997) (Athena Scientific, Belmont, MA) Google Scholar
• Bolduc Denis, Ben-Akiva Moshe. A multinomial probit formulation for large choice sets. Proc. Sixth IATBR Conf. (1991) Quebec, CanadaGoogle Scholar
• Carmone Frank, Jain Arun. Robustness of conjoint analysis: some Monte Carlo results. J. Marketing Res. (1978) 15:300–303Crossref, Google Scholar
• Carroll Douglas, Green Paul. Psychometric methods in marketing research: Part I, Conjoint analysis. J. Marketing Res. (1995) 32:385–391Crossref, Google Scholar
• Cortes Corinna, Vapnik Vladimir. Support vector networks. Machine Learning (1995) 20:1–25Crossref, Google Scholar
• Cooley R., Srivastava J., Mobasher B. Web mining: Information and pattern discovery on the World Wide Web. Proc. 9th IEEE Internat. Conf. Tools with Artificial Intelligence (ICTAI’97) (1997) Crossref, Google Scholar
• Cui Dapeng, Curry David. Prediction in marketing using the support vector machines. Marketing Sci. (2005) . ForthcomingLink, Google Scholar
• DeSarbo Wayne, Ansari Asim. Representing heterogeneity in consumer response models. Marketing Lett. (1997) 8(3):335–348Crossref, Google Scholar
• Devroye Luc, Györfi Laszlo, Lugosi Gabor. A Probabilistic Theory of Pattern Recognition, Applications of Mathematics (1996) (Springer, New York) . No. 31Crossref, Google Scholar
• Evgeniou T., Pontil M., Poggio T. Regularization networks and support vector machines. Adv. Comput. Math. (2000a) 13:1–50Crossref, Google Scholar
• Evgeniou T., Pontil M., Poggio T. Statistical learning theory: a primer. Internat. J. Comput. Vision (2000b) 38(1):9–13Crossref, Google Scholar
• Evgeniou T., Pontil M., Poggio T., Papageorgiou C. Image representations and feature selection for multimedia database search. IEEE Trans. Knowledge Data Engrg. (2003) 15:911–920Crossref, Google Scholar
• Freund Yoav, Schapire Robert. A decision-theoretic generalization of online learning and an application to boosting. J. Comput. System Sci. (1997) 55(1):119–139Crossref, Google Scholar
• Friedman Jerome, Hastie Trevor, Tibshirani Robert. Additive logistic regression: a statistical view of boosting. Ann. Statist. (2000) 28(2):337–407Crossref, Google Scholar
• Girosi Federico, Jones Michael, Poggio Tomaso. Regularization theory and neural networks architectures. Neural Comput. (1995) 7:219–269Crossref, Google Scholar
• Green Paul, Srinivasan V. Conjoint analysis in consumer research: issues and outlook. Consumer Res. (1978) 5(2):103–123Crossref, Google Scholar
• Green Paul, Srinivasan V. Conjoint analysis in marketing: new developments with implications for research and practice. J. Marketing (1990) 54(4):3–19Crossref, Google Scholar
• Herbrich Ralf, Graepel Thore, Obermayer Klaus, Smola Alexander J., Bartlett Peter, Schölkopf Bernhard, Schuurmans Dale. Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers (1999) (MIT Press, Cambridge, MA) 29–53Google Scholar
• Jedidi Kamel, Jagpal Sharan, Manchanda Puneet. Measuring heterogeneous reservation prices for product bundles. Marketing Sci. (2003) 22(1):107–130Link, Google Scholar
• Kearns Michael, Schapire Robert. Efficient distribution-free learning of probabilistic concepts. J. Comput. Systems Sci. (1994) 48(3):464–497Crossref, Google Scholar
• Kohavi Ron. Mining E-commerce data: the good, the bad, and the ugly. Proc. Seventh ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (2001) San Francisco, California(ACM Press, New York) 8–13Crossref, Google Scholar
• Kuhfeld Warren F., Tobias Randall D., Garratt Mark. Efficient experimental design with marketing research applications. J. Marketing Res. (1994) 31(4):545–557Crossref, Google Scholar
• Lenk Peter J., DeSarbo Wayne S., Green Paul E., Young Martin R. Hierarchical Bayes conjoint analysis: recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. (1996) 15:173–191Link, Google Scholar
• Louviere Jordan J., Hensher David A., Swait Joffre D.Stated Choice Methods: Analysis and Applications (2000) (Cambridge University Press, Cambridge, UK) Crossref, Google Scholar
• Manski Charles F. The structure of random utility models. Theory Decision (1977) 8:229–254Crossref, Google Scholar
• McFadden Daniel, Zarembka Paul. Conditional logit analysis of qualitative choice behavior. Frontiers in Econometrics (1974) (Academic Press, New York) 105–142Google Scholar
• McFadden Daniel. The choice theory approach to marketing research. Marketing Sci. (1986) 5(4):275–297Link, Google Scholar
• Niyogi Partha, Poggio Tomaso, Girosi Federico. Incorporating prior information in machine learning by creating virtual examples. IEEE Proc. Intelligent Signal Processing (1998) 86(11):2196–2209Google Scholar
• Oppewal H., Louviere J., Timmermans H. Modeling hierarchical conjoint processes with integrated choice experiments. J. Marketing Res. (1994) 31:92–105Crossref, Google Scholar
• Pauwels Koen. How dynamic consumer response, competitor response, company support, and company inertia shape long-term marketing effectiveness. Marketing Sci. (2004) 23(4):596–610Link, Google Scholar
• Pontil Massimiliano, Verri Alessandro. Properties of support vector machines. Neural Comput. (1998) 10:955–974Crossref, Google Scholar
• Rifkin Ryan. Everything old is new again: a fresh look at historical approaches in machine learning. (2002) . Ph.D. thesis, MIT Cambridge, MAGoogle Scholar
• Sawtooth Software, Inc. HB-Reg: Hierarchical Bayes regression. . http://www.sawtoothsoftware.com/hbreg.shtmlGoogle Scholar
• Scholkopf Bernhard, Burges Chris, Vapnik Vladimir, von der Malsburg C., von Seelen W., Vorbrüggen J. C., Sendhoff B. Incorporating invariances in support vector learning machines. Artificial Neural Networks, ICANN’96, Lecture Notes in Comput. Sci. (1996) 1112(Springer, Berlin, Germany) 47–52Crossref, Google Scholar
• Segal Madhav N. Reliability of conjoint analysis: contrasting data collection procedures. J. Marketing Res. (1982) 19:139–143Crossref, Google Scholar
• Srinivasan V., Aronson Jay E., Zionts Stanley. A strict paired comparison linear programming approach to nonmetric conjoint analysis. Oper. Res. Methods, Models and Applications (1998) (Quorum Books, Westport, CT) 97–111Google Scholar
• Srinivasan V., Shocker Allan D. Linear programming techniques for multidimensional analysis of preferences. Psychometrica (1973) 38(3):337–369Crossref, Google Scholar
• Srinivasan V., Jain Arun, Malhotra Naresh. Improving the predictive power of conjoint analysis by constrained parameter estimation. J. Marketing Res. (1983) 20:433–438Crossref, Google Scholar
• Stone C. J. Additive regression and other nonparametric models. Ann. Statist. (1985) 13:689–705Crossref, Google Scholar
• Tikhonov A. N., Arsenin V. Y.Solutions of Ill-Posed Problems (1977) (W. H. Winston, Washington, D.C) Google Scholar
• Tong Simon, Koller Daphne. Support vector machine active learning with applications to text classification. Proc. Seventeenth Internat. Conf. Machine Learning (2000) Google Scholar
• Toubia Olivier, Hauser John R., Simester Duncan I. Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. (2004) 41:116–131Crossref, Google Scholar
• Toubia Olivier, Simester Duncan I., Hauser John R., Dahan Ely. Fast polyhedral adaptive conjoint estimation. Marketing Sci. (2003) 22(3):273–303Link, Google Scholar
• Tversky Amos, Kahneman Daniel. Judgment under uncertainty: heuristics and biases. Science (1974) 185:1124–1131Crossref, Google Scholar
• Ulrich Karl T., Eppinger Steven D.Product Design and Development (2000) (McGraw-Hill, Inc., New York) Google Scholar
• Vapnik Vladimir. Statistical Learning Theory (1998) (Wiley, New York) Google Scholar
• Vapnik Vladimir, Chervonenkis Alexey. On the uniform convergence of relative frequences of events to their probabilities. Theory Probab. Appl. (1971) 17(2):264–280Crossref, Google Scholar
• Wahba Grace. Splines Models for Observational Data. Series in Applied Mathematics (1990) 59(SIAM, Philadelphia, PA) Crossref, Google Scholar
• Wittink Dick R., Cattin Philippe. Commercial use of conjoint analysis: an update. J. Marketing (1989) 53(3):91–96Crossref, Google Scholar
• Zhu Ji, Hastie Trevor. Kernel logistic regression and the import vector machine. Proc. NIPS2001 (2001) Vancouver, CanadaGoogle Scholar