Kees Mulder is a PhD on the Circular Data Modeling research group. He finished his Master’s Thesis in June 2014. His PhD, to run from 2014-2018, is funded by a VIDI grant (452-12-010) from NWO that was awarded to Irene Klugkist. The PhD Project investigates various statistical models encountered in the intrinsic approach of circular data modeling, as well as their interpretation.
Specifically, he’s worked on Bayesian circular outcome regression models, Bayesian hypothesis tests for circular data, flexible (in particular peaked) mixture models for saccade directions, interpretation of projected normal regression models, MCMC sampling for the Jones-Pewsey distribution, circular mediation models and censored data on the circle. For more info, see here. Kees is an avid programmer and package developer, as evidenced by his GitHub and CRAN.
Besides self-evident interest in Bayesian analysis and circular data, he is also interested in the general practice of research, and how it can be performed in such a way that the results will be both generalizable and valid. Also, he is interested in communication in science and how peer reviewers, editors and scientists can work together to promote good practice in fields where methodological and statistical issues in published studies are common.