Generalized robust conjoint estimation

We develop a framework within which robust models of preferences are computationally efficiently estimated using quadratic optimization methods. Within this framework general highly non-linear models can be computationally efficiently estimated while at the same time avoiding overfitting problems that such models typically have. We compare these models with standard logistic regression and recently proposed polyhedral conjoint methods.