6.4k citations · 14.8k across the 73 of their papers we have counts for
4 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…
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…
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…