This thesis discusses new mixture(-amount) models, choice models and the optimal design of experiments. Two chapters of the thesis relate to the so-called mixture, which is a product or service whose ingredients’ proportions sum to one.
The thesis begins by introducing mixture models in the choice context and develops new optimal design construction algorithms for choice experiments involving mixtures. Building further, varying the total amount of a mixture, and not only its ingredient proportions, might also affect the response.

The models that exist for mixture-amount data date back to the 1980s and have several drawbacks, which limit their usefulness for these data. Therefore, the next chapter in this thesis develops new flexible models for mixture-amount data, which are based on so-called Gaussian processes. The last chapter builds on the aforementioned model and, using revealed preference data on green vehicle purchases in France, presents a new choice model that accounts for latent environmental consciousness, where environmental consciousness is allowed to have a flexible heterogeneous impact on the vehicle choice across the population.

Additional Metadata
Keywords Choice experiment, Mixture coordinate-exchange algorithm, Particle swarm optimization, Mixture experiment, Ingredient proportions, Gaussian process prior, Nonparametric Bayes, Mixtures of ingredients, Latent environmental consciousness, Eleectric vehicle, Hybrid, Integrated choice and latent variable model (ICLV)
Promotor D. Fok (Dennis) , P.P. Goos (Peter)
Publisher Erasmus University Rotterdam
ISBN 978-90-361-0471-5
Persistent URL hdl.handle.net/1765/94978
Series Tinbergen Instituut Research Series
Citation
Ruseckaite, A. (2017, January 12). New Flexible Models and Design Construction Algorithms for Mixtures and Binary Dependent Variables (No. 670). Tinbergen Instituut Research Series. Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/94978