6.4k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- ETH ZurichCH70 papers
- Centre de Recherche en Économie et StatistiqueFR4 papers
- Max Planck Institute for Intelligent SystemsDE4 papers
- University of ZurichCH4 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- University of BernCH3 papers
- Carnegie Mellon UniversityUS2 papers
- Delft University of TechnologyNL2 papers
- Heidelberg UniversityDE2 papers
- Institute for Biomedical EngineeringCH2 papers
- Laboratoire d’Analyse et de Mathématiques AppliquéesFR2 papers
- Ludwig-Maximilians-Universität MünchenDE2 papers
6 papers · 2 filters
High dimensional sparse covariance estimation via directed acyclic graphs
Philipp Rütimann, Peter Bühlmann
We present a graph-based technique for estimating sparse covariance matrices and their inverses from high-dimensional data. The method is based on learning a directed acyclic graph…
Nonparametric inference for competing risks current status data with continuous, discrete or grouped observation times
Marloes H. Maathuis, Michael G. Hudgens
New methods and theory have recently been developed to nonparametrically estimate cumulative incidence functions for competing risks survival data subject to current status censori…
Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm
Peter Bühlmann, Markus Kalisch, Marloes H. Maathuis
We consider variable selection in high-dimensional linear models where the number of covariates greatly exceeds the sample size. We introduce the new concept of partial faithfulnes…
Regularized estimation of large-scale gene association networks using graphical Gaussian models
Nicole Kraemer, Juliane Schaefer, Anne-Laure Boulesteix
Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exce…
Decomposition and Model Selection for Large Contingency Tables
Corinne Dahinden, Markus Kalisch, Peter Bühlmann
Large contingency tables summarizing categorical variables arise in many areas. For example in biology when a large number of biomarkers are cross-tabulated according to their disc…
Missing values: sparse inverse covariance estimation and an extension to sparse regression
Nicolas Städler, Peter Bühlmann
We propose an l1-regularized likelihood method for estimating the inverse covariance matrix in the high-dimensional multivariate normal model in presence of missing data. Our metho…