Improving parameter estimates and model prediction by aggregate customization in choice experiments


Journal Article

We propose aggregate customization as an approach to improve individual estimates using a hierarchical Bayes choice model. Our approach involves the use of prior estimates to build a common design customized for the average respondent. We conduct two simulation studies to investigate conditions that are most conducive to aggregate customization. The simulations are validated by a field study showing that aggregate customization results in better estimates of individual parameters and more accurate predictions of individuals' choices. The proposed approach is easy to use, and a simulation study can assess the expected benefit from aggregate customization prior to its implementation.

Full Text

Duke Authors

Cited Authors

  • Arora, N; Huber, J

Published Date

  • September 1, 2001

Published In

Volume / Issue

  • 28 / 2

Start / End Page

  • 273 - 283

International Standard Serial Number (ISSN)

  • 0093-5301

Digital Object Identifier (DOI)

  • 10.1086/322902

Citation Source

  • Scopus