832 citations · 3k across the 34 of their papers we have counts for
7 papers · 1 filter
Statistical Theory for High-Dimensional Models
Sara van de Geer
These lecture notes consist of three chapters. In the first chapter we present oracle inequalities for the prediction error of the Lasso and square-root Lasso and briefly describe…
Discussion: "A significance test for the lasso"
Peter Bühlmann, Lukas Meier, Sara van de Geer
Discussion of "A significance test for the lasso" by Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani, Robert Tibshirani [arXiv:1301.7161].
The additive model with different smoothness for the components
Sara van de Geer, Alan Muro
We consider an additive regression model consisting of two components and , where the first component is in some sense "smoother" than the second . Smoothness…
Censored linear model in high dimensions
Patric Müller, Sara van de Geer
Censored data are quite common in statistics and have been studied in depth in the last years. In this paper we consider censored high-dimensional data. High-dimensional models are…
On higher order isotropy conditions and lower bounds for sparse quadratic forms
Sara van de Geer, Alan Muro
This study aims at contributing to lower bounds for empirical compatibility constants or empirical restricted eigenvalues. This is of importance in compressed sensing and theory fo…
Worst possible sub-directions in high-dimensional models
Sara van de Geer
We examine the rate of convergence of the Lasso estimator of lower dimensional components of the high-dimensional parameter. Under bounds on the -norm on the worst possible…
Confidence intervals for high-dimensional inverse covariance estimation
Jana Jankova, Sara van de Geer
We propose methodology for statistical inference for low-dimensional parameters of sparse precision matrices in a high-dimensional setting. Our method leads to a non-sparse estimat…