3 papers
stat.ME2026
Horseshoe Forests for High-Dimensional Causal Survival Analysis
Tijn Jacobs, Wessel N. van Wieringen, Stéphanie L. van der Pas
We develop a Bayesian tree ensemble model to estimate heterogeneous treatment effects in censored survival data with high-dimensional covariates. Instead of imposing sparsity throu…
stat.ME2026
Combined shrinkage of fixed and random effects in linear mixed models using empirical Bayes
Matteo Amestoy, R. Vermeulen, Mark A. van de Wiel +1
A novel data-driven methodology is presented for the joint selection of prior parameters for both fixed and random effects in Linear Mixed Models (LMMs). This approach facilitates…
stat.ME2025
Bayesian Profile Regression with Linear Mixed Models (Profile-LMM) applied to Longitudinal Exposome Data
Matteo Amestoy, Mark van de Wiel, Jeroen Lakerveld +1
Exposure to diverse non-genetic factors, known as the exposome, is a critical determinant of health outcomes. However, analyzing the exposome presents significant methodological ch…