832 citations · 3k across the 34 of their papers we have counts for
7 papers · 1 filter
Correlated variables in regression: clustering and sparse estimation
Peter Bühlmann, Philipp Rütimann, Sara van de Geer +1
We consider estimation in a high-dimensional linear model with strongly correlated variables. We propose to cluster the variables first and do subsequent sparse estimation such as…
Confidence sets in sparse regression
Richard Nickl, Sara van de Geer
The problem of constructing confidence sets in the high-dimensional linear model with response variables and parameters, possibly , is considered. Full honest adapt…
Quasi-Likelihood and/or Robust Estimation in High Dimensions
Sara van de Geer, Patric Müller
We consider the theory for the high-dimensional generalized linear model with the Lasso. After a short review on theoretical results in literature, we present an extension of the o…
Generic chaining and the l1-penalty
Sara van de Geer
We address the choice of the tuning parameter in -penalized M-estimation. Our main concern is models which are highly nonlinear, such as the Gaussian mixture model. The…
-penalized maximum likelihood for sparse directed acyclic graphs
Sara van de Geer, Peter Bühlmann
We consider the problem of regularized maximum likelihood estimation for the structure and parameters of a high-dimensional, sparse directed acyclic graphical (DAG) model with Gaus…
Weakly decomposable regularization penalties and structured sparsity
Sara van de Geer
It has been shown in literature that the Lasso estimator, or l1-penalized least squares estimator, enjoys good oracle properties. This paper examines which special properties of th…
L1-Penalization for Mixture Regression Models
Nicolas Städler, Peter Bühlmann, Sara van de Geer
We consider a finite mixture of regressions (FMR) model for high-dimensional inhomogeneous data where the number of covariates may be much larger than sample size. We propose an l1…