63 citations · 65 across the 5 of their papers we have counts for
5 papers
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…
Fusion of Tree-induced Regressions for Clinico-genomic Data
Jeroen M. Goedhart, Mark A. van de Wiel, Wessel N. van Wieringen +1
Cancer prognosis is often based on a set of omics covariates and a set of established clinical covariates such as age and tumor stage. Combining these two sets poses challenges. Fi…
Transfer learning of regression models from a sequence of datasets by penalized estimation
Wessel N. van Wieringen, Harald Binder
Transfer learning refers to the promising idea of initializing model fits based on pre-training on other data. We particularly consider regression modeling settings where parameter…
Lecture notes on ridge regression
Wessel N. van Wieringen
The linear regression model cannot be fitted to high-dimensional data, as the high-dimensionality brings about empirical non-identifiability. Penalized regression overcomes this no…