rags2ridges: A One-Stop-Shop for Graphical Modeling of High-Dimensional Precision Matrices

no code implementations • 12 Oct 2020 • Carel F. W. Peeters, Anders Ellern Bilgrau, Wessel N. van Wieringen

As such, it provides a one-stop-shop for graphical modeling of high-dimensional precision matrices.

Stable prediction with radiomics data

1 code implementation • 27 Mar 2019 • Carel F. W. Peeters, Caroline Übelhör, Steven W. Mes, Roland Martens, Thomas Koopman, Pim de Graaf, Floris H. P. van Velden, Ronald Boellaard, Jonas A. Castelijns, Dennis E. te Beest, Martijn W. Heymans, Mark A. van de Wiel

It outperforms other classification (and feature selection) techniques in both external and internal validation settings regarding survival in squamous cell cancers.

Classification feature selection +1

The Spectral Condition Number Plot for Regularization Parameter Determination

1 code implementation • 14 Aug 2016 • Carel F. W. Peeters, Mark A. van de Wiel, Wessel N. van Wieringen

Many modern statistical applications ask for the estimation of a covariance (or precision) matrix in settings where the number of variables is larger than the number of observations.

Vocal Bursts Type Prediction

Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes

1 code implementation • 26 Sep 2015 • Anders Ellern Bilgrau, Carel F. W. Peeters, Poul Svante Eriksen, Martin Bøgsted, Wessel N. van Wieringen

Situations are considered in which the classes are defined by data sets and subtypes of diseases.

Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data

1 code implementation • 4 Mar 2014 • Wessel N. van Wieringen, Carel F. W. Peeters

We study ridge estimation of the precision matrix in the high-dimensional setting where the number of variables is large relative to the sample size.

Methodology