Improving external validity of stated choice experiments. This project aims to deliver more accurate estimates of choice behaviour by reducing biases due to choice task complexity in surveys as well as design artefacts. Extracting 'true' preferences is challenging, not only due to possible hypothetical bias, but also due to increasingly complex choice tasks and the existence of design artefacts. This project will investigate the latter two in the context of marketing, transport, health, and envi ....Improving external validity of stated choice experiments. This project aims to deliver more accurate estimates of choice behaviour by reducing biases due to choice task complexity in surveys as well as design artefacts. Extracting 'true' preferences is challenging, not only due to possible hypothetical bias, but also due to increasingly complex choice tasks and the existence of design artefacts. This project will investigate the latter two in the context of marketing, transport, health, and environmental economics, and proposes new methodologies to extract preferences that more closely reflect true behaviour in real markets.Read moreRead less
Flexible methods for latent variable models applied to Health Economics. This project aims to develop flexible and powerful methods for estimating models containing variables that are unobserved, that is, latent. Such models are often used to capture individual heterogeneity and time dependence in data collected on individuals, with each individual observed for several time periods. Latent variables can also infer group membership, where such membership is unavailable from the data. The intended ....Flexible methods for latent variable models applied to Health Economics. This project aims to develop flexible and powerful methods for estimating models containing variables that are unobserved, that is, latent. Such models are often used to capture individual heterogeneity and time dependence in data collected on individuals, with each individual observed for several time periods. Latent variables can also infer group membership, where such membership is unavailable from the data. The intended methodology is Bayesian and based on new particle methods that allow users to select between models and predict future observations even in complex situations. The research aims to inform decision making through improved use of data in health economics and related fields.Read moreRead less