This thesis puts focus on repeated measurements and, more specific, longitudinal data, which are repeated measurements on a group of ‘subjects’ over time. The interpretation of ‘subject’ depends on the context; in our illustrations policyholders and groups of policyholders (risk this thesis considers statistical techniques for modelling such data within the framework of GLMs. Use is made of generalized linear mixed models (GLMMs) which model a transformation of the mean as a linear function of both fixed and random effects. The likelihood and Bayesian approaches to GLMMs are explained. The models are illustrated by considering ltr"
