An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs

Abstract

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.

KEY WORDS::

• Alternating sample algorithm
• Bayesian D-, A-, G-, and V-optimality
• Conjoint choice design
• Coordinate-exchange algorithm
• Minimum potential design
• Multinomial logit