This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Evgeniou, T. Articles by Zacharia, G. Search for Related Content

Generalized Robust Conjoint Estimation

Technology Management, INSEAD, Boulevard de Constance, Fontainebleau 77300, France
Open Ratings, Inc., 200 West Street, Waltham, Massachusetts 02451, USA, and Laboratory for Information and Decision Systems, MIT, Cambridge, Massachusetts 02139, USA
Open Ratings, Inc., 200 West Street, Waltham, Massachusetts 02451, USA, and Center for Biological and Computational Learning, MIT, Cambridge, Massachusetts 02139, USA
theodoros.evgeniou{at}insead.edu
boussios{at}openratings.com
lysi{at}ai.mit.edu

We introduce methods from statistical learning theory to the field of conjoint analysis for preference modeling. We present a method for estimating preference models that can be highly nonlinear and robust to noise. Like recently developed polyhedral methods for conjoint analysis, our method is based on computationally efficient optimization techniques. We compare our method with standard logistic regression, hierarchical Bayes, and the polyhedral methods using standard, widely used simulation data. The experiments show that the proposed method handles noise significantly better than both logistic regression and the recent polyhedral methods and is never worse than the best method among the three mentioned above. It can also be used for estimating nonlinearities in preference models faster and better than all other methods. Finally, a simple extension for handling heterogeneity shows promising results relative to hierarchical Bayes. The proposed method can therefore be useful, for example, for analyzing large amounts of data that are noisy or for estimating interactions among product features.

Key Words: choice models; data mining; econometric models; hierarchical Bayes analysis; marketing tools; regression and other statistical techniques
History: Received: May 28, 2003;

This article has been cited by other articles:

				T. Evgeniou, M. Pontil, and O. Toubia A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation Marketing Science, November 1, 2007; 26(6): 805 - 818. [Abstract] [PDF]

				O. Toubia and J. R. Hauser Research Note On Managerially Efficient Experimental Designs Marketing Science, November 1, 2007; 26(6): 851 - 858. [Abstract] [PDF]

				O. Toubia, J. Hauser, and R. Garcia Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application Marketing Science, September 1, 2007; 26(5): 596 - 610. [Abstract] [PDF]

				M. Yee, E. Dahan, J. R. Hauser, and J. Orlin Greedoid-Based Noncompensatory Inference Marketing Science, July 1, 2007; 26(4): 532 - 549. [Abstract] [PDF]

				O. Toubia and L. Flores Adaptive Idea Screening Using Consumers Marketing Science, May 1, 2007; 26(3): 342 - 360. [Abstract] [PDF]

				R. Kohli and K. Jedidi Representation and Inference of Lexicographic Preference Models and Their Variants Marketing Science, May 1, 2007; 26(3): 380 - 399. [Abstract] [PDF]

				L. Luo, P. K. Kannan, and B. T. Ratchford New Product Development Under Channel Acceptance Marketing Science, March 1, 2007; 26(2): 149 - 163. [Abstract] [PDF]

				B. Sun Invited Commentary--Dynamic Structural Consumer Models and Current Marketing Issues Marketing Science, November 1, 2006; 25(6): 625 - 628. [Abstract] [PDF]

				S. M. Shugan Editorial: Who Is Afraid to Give Freedom of Speech to Marketing Folks? Marketing Science, September 1, 2006; 25(5): 403 - 410. [Abstract] [PDF]

				S. M. Shugan Editorial: Errors in the Variables, Unobserved Heterogeneity, and Other Ways of Hiding Statistical Error Marketing Science, May 1, 2006; 25(3): 203 - 216. [Abstract] [PDF]

				D. Cui and D. Curry Prediction in Marketing Using the Support Vector Machine Marketing Science, January 1, 2005; 24(4): 595 - 615. [Abstract] [PDF]