Kumulative Dissertation, Otto-Friedrich-Universität Bamberg, 2026

Empirical research often works with data coarser than the question demands: a survey too sparse for district-level estimates, geocoordinates collapsed into regional totals, a continuous preference recorded on a five-point scale. This cumulative dissertation treats these as one problem — coarsened data — and recovers fine-grained structure by combining the coarse observation with auxiliary information.

Chapter 1 estimates district-level person-kilometers traveled from the German household travel survey Mobilität in Deutschland 2017 with a log-transformed Fay-Herriot model across 1,590 domains. Covariates from official statistics and the INKAR database are selected in a three-phase LASSO procedure, reducing coefficients of variation by 16–44% against direct estimation.

Chapter 2 extends kernel density estimation from aggregated data by integrating auxiliary spatial information through a correlation-based weighting scheme. The estimator is assessed in model-based simulations and in an evaluation study on population densities from the 2022 German Census in Bavaria, and then applied to brown hare hunting bag data in Lower Saxony.

Chapter 3 applies the mixed-effects frequency-adjusted borders ordinal forest to lexical preference ratings in Maltese English and contributes partial dependence routines in R. It recovers curved, non-monotonic age trajectories that the cumulative link mixed model does not.