Jolien began working on circular statistics in 2013 while writing her master’s thesis. In this thesis she assessed a Bayesian embedding approach to circular regression models. In 2014 she started her PhD that is focused on developing Bayesian methods for the analysis of circular longitudinal data. This PhD project focuses on models within the embedding approach to circular data is funded by a VIDI grant (452-12-010) from NWO that was awarded to Irene Klugkist.

In particular, Jolien has done research on Bayesian projected normal (PN) regression models and their interpretation, the application of circular statistics to circumplex data, distinguishing between ‘accuracy’ and ‘location’ effects in PN regression models, Bayesian PN mixed-effects models and models for cylindrical data with an application to the interpersonal circumplex. She has also written an R-package, bpnreg, for Bayesian PN regression and mixed-effects models.

Links:
E-mail
GitHub
University profile
LinkedIn