PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation
Theodoros Evgeniou, Massimiliano Pontil and Olivier Toubia
Marketing Science 2007.

Abstract

We propose and test a new approach for modeling consumer heterogeneity in con- joint estimation based on convex optimization and statistical machine learning. We develop methods both for metric and choice data. Like hierarchical Bayes (HB), our methods shrink individual-level partworth estimates towards a population mean. However, while HB samples from a posterior distribution that is in°uenced by ex- ogenous parameters (the parameters of the second-stage priors), we minimize a convex loss function that depends only on endogenous parameters. As a result, the amounts of shrinkage di®er between the two approaches, leading to different estimation accuracies. In our comparisons based on simulations as well as empirical data sets, the new approach overall outperforms standard HB (i.e., with relatively di®use second-stage priors) both with metric and choice data.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
User Modelling for Computer Human Interaction
Information Retrieval & Textual Information Access
ID Code:3784
Deposited By:Massimiliano Pontil
Deposited On:22 February 2008