Generalized Robust Conjoint Estimation

  • Authors:

  • Theodoros Evgeniou;Constantinos Boussios;Giorgos Zacharia

  • Affiliations:

  • 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

  • Venue:
  • Marketing Science
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.