3 papers
stat.ME2026
Distinguishing case-mix from context heterogeneity in prognostic regression model synthesis settings
Max Behrens, Janis M. Nolde, Eleni Papakonstantinou +6
Prognostic regression models often synthesize data from multiple sites, whether within a multi-site study, across federated settings, or in individual participant data meta-analysi…
math.PR2026
Before and beyond the mixing time: New approximations for additive functionals of stationary Gauss-Markov processes
Gabriele Bellerino, Angelika Rohde
Whereas classical invariance principles for ergodic Markov chains address the situation in which the time horizon of observations is much larger than the mixing time, the quality o…
stat.ME2026
Contrasting Global and Patient-Specific Regression Models via a Neural Network Representation
Max Behrens, Daiana Stolz, Eleni Papakonstantinou +5
When developing clinical prediction models, it can be challenging to balance between global models that are valid for all patients and personalized models tailored to individuals o…