4 papers
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…
How important are the genes to explain the outcome - the asymmetric Shapley value as an honest importance metric for high-dimensional features
Mark A. van de Wiel, Jeroen Goedhart, Martin Jullum +1
In clinical prediction settings the importance of a high-dimensional feature like genomics is often assessed by evaluating the change in predictive performance when adding it to a…
A Semi-supervised CART Model for Covariate Shift
Mingyang Cai, Thomas Klausch, Mark A. van de Wiel
Machine learning models used in medical applications often face challenges due to the covariate shift, which occurs when there are discrepancies between the distributions of traini…
Co-data Learning for Bayesian Additive Regression Trees
Jeroen M. Goedhart, Thomas Klausch, Jurriaan Janssen +1
Medical prediction applications often need to deal with small sample sizes compared to the number of covariates. Such data pose problems for prediction and variable selection, espe…