@article{kessels_goos_vandebroek_2008, title={Optimal designs for conjoint experiments}, volume={52}, ISSN={["1872-7352"]}, DOI={10.1016/j.csda.2007.10.016}, abstractNote={In conjoint experiments, each respondent receives a set of profiles to rate. Sometimes, the profiles are expensive prototypes that respondents have to test before rating them. Designing these experiments involves determining how many and which profiles each respondent has to rate and how many respondents are needed. To that end, the set of profiles offered to a respondent is treated as a separate block in the design and a random respondent effect is used in the model because profile ratings from the same respondent are correlated. Optimal conjoint designs are then obtained by means of an adapted version of an algorithm for finding D-optimal split-plot designs. A key feature of the design construction algorithm is that it returns the optimal number of respondents and the optimal number of profiles each respondent has to evaluate for a given number of profiles. The properties of the optimal designs are described in detail and some practical recommendations are given.}, number={5}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={Kessels, Roselinde and Goos, Peter and Vandebroek, Martina}, year={2008}, month={Jan}, pages={2369–2387} }
 @article{kessels_jones_goos_vandebroek_2008, title={Recommendations on the Use of Bayesian Optimal Designs for Choice Experiments}, volume={24}, ISSN={["0748-8017"]}, DOI={10.1002/qre.953}, abstractNote={Abstract 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 provide practical advice on how to properly specify the prior parameter distribution. Also, we present an example to illustrate that Bayesian optimal designs generally outperform utility‐neutral optimal designs that are based on linear design principles. Copyright © 2008 John Wiley & Sons, Ltd.}, number={6}, journal={QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL}, author={Kessels, Roselinde and Jones, Bradley and Goos, Peter and Vandebroek, Martina}, year={2008}, month={Oct}, pages={737–744} }
 @article{kessels_goos_vandebroek_2006, title={A comparison of criteria to design efficient choice experiments}, volume={43}, ISSN={["0022-2437"]}, DOI={10.1509/jmkr.43.3.409}, abstractNote={To date, no attempt has been made to design efficient choice experiments by means of the G- and V-optimality criteria. These criteria are known to make precise response predictions, which is exactly what choice experiments aim to do. In this article, the authors elaborate on the G- and V-optimality criteria for the multinomial logit model and compare their prediction performances with those of the D- and A-optimality criteria. They make use of Bayesian design methods that integrate the optimality criteria over a prior distribution of likely parameter values. They employ a modified Fedorov algorithm to generate the optimal choice designs. They also discuss other aspects of the designs, such as level overlap, utility balance, estimation performance, and computational effectiveness.}, number={3}, journal={JOURNAL OF MARKETING RESEARCH}, author={Kessels, Roselinde and Goos, Peter and Vandebroek, Martina}, year={2006}, month={Aug}, pages={409–419} }