Asim Ansari

Hierachical Bayes Versus Finite Mixture Conjoint Analysis Models: A Comparision of Fit, Prediction, and Part-Worth Recovery

Coauthor(s): Rick Andrews, Imran Currim. View Publication

Abstract:
No study has compared the relative effectiveness of finite mixture and hierarchical Bayes conjoint analysis models in terms of fit, prediction, and parameter recovery. To conduct such a comparison, the authors employ the simulation methodology proposed by Vriens, Wedel, and Wilms with some modification. The authors estimate traditional individual-level conjoint models as well. The authors show that FM and HB models are equally effective in recovering individual-level parameters and predicting ratings of holdout profiles. Two surprising findings are that: 1. HB performs well even when partworths come from a mixture of distributions and, 2. FM produces good parameter estimates, even at the individual level. The authors show that both models are quite robust to violations of underlying assumptions and that traditional individual-level models overfit the data.

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