105 citations · 169 across the 6 of their papers we have counts for
9 papers
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…