TY - JOUR
AU - Huber, Joel
AB - 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.
TI - Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments
JF - Journal of Consumer Research
DO - 10.1086/322902
DA - 2001-09-01
UR - https://www.deepdyve.com/lp/oxford-university-press/improving-parameter-estimates-and-model-prediction-by-aggregate-1U5exczF6M
SP - 273
EP - 283
VL - 28
IS - 2
DP - DeepDyve
ER -