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Circular Data Modelling

PhD project: Jolien Cremers

Circular data in longitudinal designs

This project focuses on developing Bayesian methods for the analysis of circular longitudinal data. Several methods have already been proposed in the literature and the research in this project will focus on investigating and elaborating on those methods. In the first part of the project, a Bayesian embedding approach to circular longitudinal data using a mixed effects model is investigated. Tools for interpreting the results from this model will be developed in such a way that applied researchers may use them for their own data (e.g. to assess the size of a circular fixed or random effect and perform hypothesis tests).  Additionally, tools for model comparison will also be developed. In the future, alternative approaches and extensions to the embedding approach to circular longitudinal data will be investigated.