105 citations · 177 across the 11 of their papers we have counts for
11 papers
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…
Fast marginal likelihood estimation of penalties for group-adaptive elastic net
Mirrelijn M. van Nee, Tim van de Brug, Mark A. van de Wiel
Nowadays, clinical research routinely uses omics data, such as gene expression, for predicting clinical outcomes or selecting markers. Additionally, so-called co-data are often ava…
Fast cross-validation for multi-penalty ridge regression
Mark A. van de Wiel, Mirrelijn M. van Nee, Armin Rauschenberger
High-dimensional prediction with multiple data types needs to account for potentially strong differences in predictive signal. Ridge regression is a simple model for high-dimension…
Stable prediction with radiomics data
Carel F. W. Peeters, Caroline Übelhör, Steven W. Mes +9
Motivation: Radiomics refers to the high-throughput mining of quantitative features from radiographic images. It is a promising field in that it may provide a non-invasive solution…
Estimation of variance components, heritability and the ridge penalty in high-dimensional generalized linear models
Jurre R. Veerman, Gwenael G. R. Leday, Mark A. van de Wiel
For high-dimensional linear regression models, we review and compare several estimators of variances and of the random slopes and errors, respectively. These variances…
Estimating Bayesian Optimal Treatment Regimes for Dichotomous Outcomes using Observational Data
Thomas Klausch, Peter van de Ven, Tim van de Brug +2
Optimal treatment regimes (OTR) are individualised treatment assignment strategies that identify a medical treatment as optimal given all background information available on the in…
Detecting SNPs with interactive effects on a quantitative trait
Armin Rauschenberger, Renee X. Menezes, Mark A. van de Wiel +2
Here we propose a test to detect effects of single nucleotide polymorphisms (SNPs) on a quantitative trait. Significant SNP-SNP interactions are more difficult to detect than signi…
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…
Optimal weighting for false discovery rate control
Etienne Roquain, Mark Van De Wiel
How to weigh the Benjamini-Hochberg procedure? In the context of multiple hypothesis testing, we propose a new step-wise procedure that controls the false discovery rate (FDR) and…
A nonparametric control chart based on the Mann-Whitney statistic
Subhabrata Chakraborti, Mark A. van de Wiel
Nonparametric or distribution-free charts can be useful in statistical process control problems when there is limited or lack of knowledge about the underlying process distribution…