4 citations · 4 across the 1 of their papers we have counts for
3 papers
Semi-supervised empirical Bayes group-regularized factor regression
Magnus M. Münch, Mark A. van de Wiel, Aad W. van der Vaart +1
The features in high dimensional biomedical prediction problems are often well described with lower dimensional manifolds. An example is genes that are organised in smaller functio…
Adaptive group-regularized logistic elastic net regression
Magnus M. Münch, Carel F. W. Peeters, Aad W. van der Vaart +1
In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (a) p-values from a previ…
Learning from a lot: Empirical Bayes in high-dimensional prediction settings
Mark A. van de Wiel, Dennis E. te Beest, Magnus Münch
Empirical Bayes is a versatile approach to `learn from a lot' in two ways: first, from a large number of variables and second, from a potentially large amount of prior information,…