2 papers
stat.AP2017
Improved high-dimensional prediction with Random Forests by the use of co-data
Dennis E. te Beest, Steven W. Mes, Ruud H. Brakenhoff +1
Prediction in high dimensional settings is difficult due to large by number of variables relative to the sample size. We demonstrate how auxiliary "co-data" can be used to improve…
stat.ME2014
Better prediction by use of co-data: Adaptive group-regularized ridge regression
Mark A. van de Wiel, Tonje G. Lien, Wina Verlaat +2
For many high-dimensional studies, additional information on the variables, like (genomic) annotation or external p-values, is available. In the context of binary and continuous pr…