4 papers
Births are difficult to predict even with rich survey and full-population register data
Elizaveta Sivak, Emily M. Cantrell, Thomas Emery +109
Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child w…
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…
ProfileGLMM: a R Package Extending Bayesian Profile Regression using Generalised Linear Mixed Models
Matteo Amestoy, Mark A. van de Wiel, Wessel N. van Wieringen
ProfileGLMM is an R package integrating Generalised Linear Mixed Models (GLMMs) as the outcome model for Bayesian profile regression. This statistical framework simultaneously i) e…
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…