An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs

Roselinde Kessels*, Bradley Jones, Peter Goos, Martina Vandebroek

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

While Bayesian G- and V-optimal designs for the multinomial logit model have been shown to have better predictive performance than Bayesian D- and A-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 criterial 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.
Original languageEnglish
Pages (from-to)279-291
Number of pages13
JournalJournal of Business & Economic Statistics
Volume27
Issue number2
DOIs
Publication statusPublished - Apr 2009
Externally publishedYes

Keywords

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

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