M. V. D. Wiel

发表

Optimal weighting for false discovery rate control

Étienne Roquain, M. V. D. Wiel, M. A. Wiel, 2008, 0807.4081.

Learning from a lot: Empirical Bayes in high-dimensional prediction settings

Magnus M. Munch, M. V. D. Wiel, D. T. Beest, 2017, 1709.04192.

Conditional Inference Procedures in a Permutation Test Framework

K. Hornik, T. Hothorn, A. Zeileis, 2015 .

Detecting SNPs with interactive effects on a quantitative trait

M. V. D. Wiel, M. Jonker, Armin Rauschenberger, 2018, 1805.09175.

Achim Zeileis A Lego System for Conditional Inference Paper

K. Hornik, T. Hothorn, A. Zeileis, 2017 .

Estimating Error and Prior Variance in a High-Dimensional Ridge Regression Models With Applications to Bayesian Structural Equation Models for Finding Treatment Targets.

M. V. D. Wiel, J. R. Veerman, M. A. Wiel, 2018 .

Exact distributions of distribution-free test statistics

M. V. D. Wiel, M. A. Wiel, van de Wiel, 2000 .

coin : A Computational Framework for Conditional Inference

K. Hornik, T. Hothorn, A. Zeileis, 2009 .

Exact distributions of two-sample rank statistics and block rank statistics using computer algebra

M. V. D. Wiel, M. A. Wiel, 1998 .

Estimation of predictive performance in high-dimensional data settings using learning curves

M. V. D. Wiel, T. Klausch, Jeroen M. Goedhart, 2022, Comput. Stat. Data Anal..

GRridge : Adaptive group-regularized ridge regression by use of codata

M. V. D. Wiel, P. Novianti, M. A. Wiel, 2017 .

Penalised regression with multiple sources of prior effects

E. Glaab, M. V. D. Wiel, Z. Landoulsi, 2022, 2212.08581.

Implementing a Class of Permutation Tests: The coin Package

Kurt Hornik, Achim Zeileis, Torsten Hothorn, 2008 .

Effects of dependence in high-dimensional multiple testing problems

Kyung In Kim, Mark A. van de Wiel, M. V. D. Wiel, 2008, BMC Bioinformatics.

CGHpower: exploring sample size calculations for chromosomal copy number experiments

Bauke Ylstra, Mark A. van de Wiel, Gerrit Meijer, 2010, BMC Bioinformatics.

A Lego System for Conditional Inference ✯

K. Hornik, T. Hothorn, A. Zeileis, 2019 .

Improved high-dimensional prediction with Random Forests by the use of co-data

Ruud H. Brakenhoff, Mark A. van de Wiel, Saskia M. Wilting, 2017, BMC Bioinformatics.

Exact null distributions of distribution-free quadratic t-sample statistics

M. V. D. Wiel, A. D. Bucchianico, 2005 .

Fast cross-validation for multi-penalty ridge regression

Mark A. van de Wiel, Armin Rauschenberger, Mirrelijn M. van Nee, 2020, 2005.09301.

ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs

Mark A. van de Wiel, Henk M. W. Verheul, Maarten Neerincx, 2014, BMC Bioinformatics.

Co-data Learning for Bayesian Additive Regression Trees

M. V. D. Wiel, Jeroen M. Goedhart, Thomas Klausch, 2023, ArXiv.

Think before you shrink: Alternatives to default shrinkage methods can improve prediction accuracy, calibration and coverage

M. Heymans, M. V. D. Wiel, E. Zwet, 2023, 2301.09890.

Fast Marginal Likelihood Estimation of Penalties for Group-Adaptive Elastic Net

Mark A. van de Wiel, Mirrelijn M. van Nee, Tim van de Brug, 2021, J. Comput. Graph. Stat..

Predictive and interpretable models via the stacked elastic net

Enrico Glaab, Armin Rauschenberger, Mark van de Wiel, 2020, Bioinform..