In conjoint analysis consumers utility functions over multiattributed stimuli are estimated using experimental data. The quality of these estimations heavily depends on the alternatives presented in the experiment. An efficient selection of the experiment design matrix allows more information to be elicited about consumer preferences from a small number of questions, thus reducing experimental cost and respondent’s fatigue. Kiefer’s methodology considers approximate optimal design selecting the same combination of stimuli more than once. In the context of conjoint analysis, replications do not make sense for individual respondents. We present a general approach to compute optimal designs for conjoint experiments in a variety of scenarios and methodologies: continuous, discrete and mixed attributes types, customer panels with random effects, and quantile regression models. We do not compute good designs, but the best ones according to the size (determinant or trace) of the information matrix of the associated estimators without repeating profiles as in Kiefer’s methodology. We use efficient optimization algorithms to achieve our goal.
|Publication status||Published - 2012|
|Event||5th International Conference of the ERCIM Working Group on Computing & Statistics 2012 - Conference Center “Ciudad de Oviedo", Oviedo|
Duration: 1 Dec 2012 → 3 Dec 2012
Conference number: 5
|Conference||5th International Conference of the ERCIM Working Group on Computing & Statistics 2012|
|Period||1/12/12 → 3/12/12|