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
25 papers · 1 filter
Graphical tools for selecting conditional instrumental sets
Leonard Henckel, Martin Buttenschön, Marloes H. Maathuis
We consider the efficient estimation of total causal effects in the presence of unmeasured confounding using conditional instrumental sets. Specifically, we consider the two-stage…
Honest calibration assessment for binary outcome predictions
Timo Dimitriadis, Lutz Duembgen, Alexander Henzi +2
Probability predictions from binary regressions or machine learning methods ought to be calibrated: If an event is predicted to occur with probability , it should materialize wi…
Various New Inequalities for Beta Distributions
Alexander Henzi, Lutz Duembgen
This note provides some new inequalities and approximations for beta distributions, including tail inequalities, exponential inequalities of Hoeffding and Bernstein type, Gaussian…
On the robustness of the minimum interpolator
Geoffrey Chinot, Matthieu Lerasle
We analyse the interpolator with minimal -norm in a general high dimensional linear regression framework where where is a ran…
Profile least squares estimators in the monotone single index model
Fadoua Balabdaoui, Piet Groeneboom
We consider least squares estimators of the finite regression parameter in the single index regression model , where is a -dimensional random vector, $\E(Y…
Oracle inequalities for square root analysis estimators with application to total variation penalties
Francesco Ortelli, Sara van de Geer
Through the direct study of the analysis estimator we derive oracle inequalities with fast and slow rates by adapting the arguments involving projections by Dalalyan, Hebiri and Le…
Sparse spectral estimation with missing and corrupted measurements
Andreas Elsener, Sara van de Geer
Supervised learning methods with missing data have been extensively studied not just due to the techniques related to low-rank matrix completion. Also in unsupervised learning one…
Spectral thresholding for the estimation of Markov chain transition operators
Matthias Löffler, Antoine Picard
We consider nonparametric estimation of the transition operator of a Markov chain and its transition density where the singular values of are assumed to decay exponenti…
Score estimation in the monotone single index model
Fadoua Balabdaoui, Piet Groeneboom, Kim Hendrickx
We consider estimation in the single index model where the link function is monotone. For this model a profile least squares estimator has been proposed to estimate the unknown lin…
Asymptotic Confidence Regions for High-dimensional Structured Sparsity
Benjamin Stucky, Sara van de Geer
In the setting of high-dimensional linear regression models, we propose two frameworks for constructing pointwise and group confidence sets for penalized estimators which incorpora…
Least squares estimation in the monotone single index model
F. Balabdaoui, C. Durot, H. Jankowski
We study the monotone single index model where a real response variable is linked to a -dimensional covariate through the relationship almost…
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…
On the Prediction Performance of the Lasso
Arnak S. Dalalyan, Mohamed Hebiri, Johannes Lederer
Although the Lasso has been extensively studied, the relationship between its prediction performance and the correlations of the covariates is not fully understood. In this paper,…
Quantifying identifiability in independent component analysis
Alexander Sokol, Marloes H. Maathuis, Benjamin Falkeborg
We are interested in consistent estimation of the mixing matrix in the ICA model, when the error distribution is close to (but different from) Gaussian. In particular, we consider…
Hierarchical Testing in the High-Dimensional Setting with Correlated Variables
Jacopo Mandozzi, Peter Bühlmann
We propose a method for testing whether hierarchically ordered groups of potentially correlated variables are significant for explaining a response in a high-dimensional linear mod…
On the uniform convergence of empirical norms and inner products, with application to causal inference
Sara van de Geer
Uniform convergence of empirical norms - empirical measures of squared functions - is a topic which has received considerable attention in the literature on empirical processes. Th…
Particle filters
Hans R. Künsch
This is a short review of Monte Carlo methods for approximating filter distributions in state space models. The basic algorithm and different strategies to reduce imbalance of the…
Hypersurfaces and their singularities in partial correlation testing
Shaowei Lin, Caroline Uhler, Bernd Sturmfels +1
An asymptotic theory is developed for computing volumes of regions in the parameter space of a directed Gaussian graphical model that are obtained by bounding partial correlations.…
A robust, adaptive M-estimator for pointwise estimation in heteroscedastic regression
Michaël Chichignoud, Johannes Lederer
We introduce a robust and fully adaptive method for pointwise estimation in heteroscedastic regression. We allow for noise and design distributions that are unknown and fulfill ver…
Asymptotic optimality of the Westfall--Young permutation procedure for multiple testing under dependence
Nicolai Meinshausen, Marloes H. Maathuis, Peter Bühlmann
Test statistics are often strongly dependent in large-scale multiple testing applications. Most corrections for multiplicity are unduly conservative for correlated test statistics,…
Oracle Inequalities and Optimal Inference under Group Sparsity
Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov +1
We consider the problem of estimating a sparse linear regression vector under a gaussian noise model, for the purpose of both prediction and model selection. We assume that p…
The Smooth-Lasso and other -penalized methods
Mohamed Hebiri, Sara A. Van De Geer
We consider a linear regression problem in a high dimensional setting where the number of covariates can be much larger than the sample size . In such a situation, one often…
On the conditions used to prove oracle results for the Lasso
Sara A. van de Geer, Peter Bühlmann
Oracle inequalities and variable selection properties for the Lasso in linear models have been established under a variety of different assumptions on the design matrix. We show in…
A statistical framework for differential privacy
Larry Wasserman, Shuheng Zhou
One goal of statistical privacy research is to construct a data release mechanism that protects individual privacy while preserving information content. An example is a {\em random…