3 papers
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.ME2026
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…
stat.ME2023
Linked shrinkage to improve estimation of interaction effects in regression models
Mark A. van de Wiel, Matteo Amestoy, Jeroen Hoogland
We address a classical problem in statistics: adding two-way interaction terms to a regression model. As the covariate dimension increases quadratically, we develop an estimator th…