The authors propose a fast and efficient algorithm for constructing D-optimal conjoint choice designs for mixed logit models in the presence of respondent heterogeneity. With this new algorithm, the construction of semi-Bayesian D-optimal mixed logit designs with large numbers of attributes and...

In this paper, we propose an efficient individually adapted sequential Bayesian approach for constructing conjoint choice experiments. It uses Bayesian updating, a Bayesian analysis and a Bayesian design criterion for generating choice-set-designs for each individual respondent based on previous...

The concept of willingness-to-pay (WTP) has attracted the attention of marketeers because of its use-fulness in many applications. Nowadays one aims at describing the market heterogeneity by estimating the distribution of WTP. However, this poses several problems that have been discussed...

In this paper, we argue that some of the prior parameter distributions used in the literature for the construction of Bayesian optimal designs are internally inconsistent. We rectify this error and provide practical advice on how to properly specify the prior parameter distribution. Also, we...

In a classical conjoint choice experiment, respondents choose one profile from each choice set that has to be evaluated. However, in real life the respondent does not always make a choice: often he/she does not prefer any of the alternatives offered. Therefore, including a no-choice option in a...

The goal of robust parameter design experiments is to identify significant location and dispersion factors that can be used to set the mean response at the target level and to decrease the sensitivity of the response to uncontrolled noise factors. We present a hierarchical Bayesian model and use...