We model the log-cumulative baseline hazard for the Cox model via Bayesian, monotonic P-splines. This approach permits fast computation, accounting for arbitrary censorship and the inclusion of nonparametric effects. We leverage the computational efficiency to simplify effect interpretation for...

We propose a fast approximate Metropolis-Hastings algorithm for large data sets embedded in a design based approach. Here, the loglikelihood ratios involved in the Metropolis-Hastings acceptance step are considered as data. The building block is one single subsample from the complete data set,...

We study the regional distribution of primary care physicians in Germany to learn about the extent and possible reasons of geographic maldistribution. For this aim, we apply a greedy capacitated algorithm on very fine spatial data. We compare this reference allocation of primary care physicians...