Alexander Aue

Inference for sparsely sampled Gauss-Markov processes under outcome-dependent dropout

This work develops a likelihood-based estimation framework that, in contrast to existing moment-based methods, avoids the bias induced by outcome-dependent dropout and demonstrates the advantages of a fully probabilistic functional data model for tasks beyond second-order inference.