10 citations · 55 across the 21 of their papers we have counts for
5 papers · 1 filter
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…
A framework for interpretation and testing of sparse canonical correlations
Nuria Senar, Mark van de Wiel, Aeilko Zwinderman +1
In clinical and biomedical research, multiple high-dimensional datasets are nowadays routinely collected from omics and imaging devices. Multivariate methods, such as Canonical Cor…
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…
Informative co-data learning for high-dimensional Horseshoe regression
Claudio Busatto, Mark van de Wiel
High-dimensional data often arise from clinical genomics research to infer relevant predictors of a particular trait. A way to improve the predictive performance is to include info…
Think before you shrink: Alternatives to default shrinkage methods can improve prediction accuracy, calibration and coverage
Mark A. van de Wiel, Gwenaël G. R. Leday, Jeroen Hoogland +3
While shrinkage is essential in high-dimensional settings, its use for low-dimensional regression-based prediction has been debated. It reduces variance, often leading to improved…