An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs

By Roselinde Kessels, Bradley A. Jones, Peter Goos, Martina Vandebroek. 2009

103 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c87297e034b391a16bcaf5193e67c758e6bc4c6d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Bradley A. Jones
2. John M. Rose
3. Michiel C. J. Bliemer
4. Peter Goos
5. Roselinde Kessels

In Economics and Econometrics

1. John M. Rose
2. Michiel C. J. Bliemer
3. Riccardo Scarpa
4. David A. Hensher
5. N. Hanley
6. Peter Goos
7. Roselinde Kessels
8. Emily Lancsar

In Management Science and Operations Research

1. John M. Rose
2. Michiel C. J. Bliemer
3. Bradley A. Jones
4. Peter Goos
5. Riccardo Scarpa
6. Roselinde Kessels
7. Emily Lancsar
8. Deborah A. Marshall

Citations in the corpus

• Bayesian optimal designs for discrete choice experiments with partial profiles (2011, methodology): “Huber and Zwerina (1996), Sándor and Wedel (2001), Kessels et al. (2006, 2009, 2011a), Scarpa and Rose (2008), among others, implemented different design criteria or functions of the information matrix (5) for constructing optimal discrete choice designs.”
• Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. (2013, background): “[57] Kessels R, Jones B, Goos P, et al.”
• Sample size requirements for stated choice experiments (2013, background): “With exogenous stratified random sampling (ESRS), the population is first divided into G mutually exclusive groups, each representing a proportion of the total population, Wg: As discussed in Louviere et al. (2000) and Hensher et al.”
• Contemporary Guidance for Stated Preference Studies (2017, methodology): “Design uncertainty can be reduced using information obtained from previous work and pilot studies, and Bayesian design methods may be applied to formally reflect uncertainty in expected parameter values (Kessels et al. 2009; Rose, Scarpa, and Bliemer 2009).”
• Nudging Energy Efficiency Behavior: The Role of Information Labels (2014, unknown): “…but also depends on the underlying choice probabilities, the choice sets fielded for each treatment were constructed using Bayesian methods for statistically efficient experimental design (Sándor and Wedel 2002; Kessels, Goos, and Vandebroek 2006; Ferrini and Scarpa 2007; Kessels et al. 2009).”