While Bayesian - and -optimal designs for the multinomial logit model have been shown to have better predictive performance than Bayesian - and -optimal designs, the algorithms for generating them have been too slow for commercial use. In this article, we present a much faster algorithm for generating Bayesian optimal designs for all four criteria while simultaneously improving the statistical efficiency of the designs. We also show how to augment a choice design allowing for correlated parameter estimates using a sports club membership study.

ERIM Top-Core Articles
Journal of Business and Economic Statistics
Erasmus Research Institute of Management

Kessels, R., Jones, B., Goos, P., & Vandebroek, M. (2009). An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs. Journal of Business and Economic Statistics, 27(2), 279–291. Retrieved from http://hdl.handle.net/1765/19557