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
27 papers · 1 filter
Assessing replicability with the sceptical p-value: Type-I error control and sample size planning
Charlotte Micheloud, Fadoua Balabdaoui, Leonhard Held
We study a statistical framework for replicability based on a recently proposed quantitative measure of replication success, the sceptical -value. A recalibration is proposed to…
Treatment Effect Estimation with Observational Network Data using Machine Learning
Corinne Emmenegger, Meta-Lina Spohn, Timon Elmer +1
Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in s…
Higher-order least squares: assessing partial goodness of fit of linear causal models
Christoph Schultheiss, Peter Bühlmann, Ming Yuan
We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular,…
Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements
Corinne Emmenegger, Peter Bühlmann
Traditionally, spline or kernel approaches in combination with parametric estimation are used to infer the linear coefficient (fixed effects) in a partially linear mixed-effects mo…
Regularizing Double Machine Learning in Partially Linear Endogenous Models
Corinne Emmenegger, Peter Bühlmann
The linear coefficient in a partially linear model with confounding variables can be estimated using double machine learning (DML). However, this DML estimator has a two-stage leas…
Distributional Anchor Regression
Lucas Kook, Beate Sick, Peter Bühlmann
Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable b…
Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)
Solt Kovács, Housen Li, Peter Bühlmann
In this discussion, we compare the choice of seeded intervals and that of random intervals for change point segmentation from practical, statistical and computational perspectives.…
Seeded Binary Segmentation: A general methodology for fast and optimal change point detection
Solt Kovács, Housen Li, Peter Bühlmann +1
In recent years, there has been an increasing demand on efficient algorithms for large scale change point detection problems. To this end, we propose seeded binary segmentation, an…
GGM knockoff filter: False Discovery Rate Control for Gaussian Graphical Models
Jinzhou Li, Marloes H. Maathuis
We propose a new method to learn the structure of a Gaussian graphical model with finite sample false discovery rate control. Our method builds on the knockoff framework of Barber…
High-dimensional simultaneous inference with the bootstrap
Ruben Dezeure, Peter Bühlmann, Cun-Hui Zhang
We propose a residual and wild bootstrap methodology for individual and simultaneous inference in high-dimensional linear models with possibly non-Gaussian and heteroscedastic erro…
Most Likely Transformations
Torsten Hothorn, Lisa Möst, Peter Bühlmann
We propose and study properties of maximum likelihood estimators in the class of conditional transformation models. Based on a suitable explicit parameterisation of the uncondition…
backShift: Learning causal cyclic graphs from unknown shift interventions
Dominik Rothenhäusler, Christina Heinze, Jonas Peters +1
We propose a simple method to learn linear causal cyclic models in the presence of latent variables. The method relies on equilibrium data of the model recorded under a specific ki…
High-dimensional inference in misspecified linear models
Peter Bühlmann, Sara van de Geer
We consider high-dimensional inference when the assumed linear model is misspecified. We describe some correct interpretations and corresponding sufficient assumptions for valid as…
A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees
Michaël Chichignoud, Johannes Lederer, Martin Wainwright
We introduce a novel scheme for choosing the regularization parameter in high-dimensional linear regression with Lasso. This scheme, inspired by Lepski's method for bandwidth selec…
Group-bound: confidence intervals for groups of variables in sparse high-dimensional regression without assumptions on the design
Nicolai Meinshausen
It is in general challenging to provide confidence intervals for individual variables in high-dimensional regression without making strict or unverifiable assumptions on the design…
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…
Bridging the ensemble Kalman and particle filter
Marco Frei, Hans R. Künsch
In many applications of Monte Carlo nonlinear filtering, the propagation step is computationally expensive, and hence, the sample size is limited. With small sample sizes, the upda…
Two Optimal Strategies for Active Learning of Causal Models from Interventional Data
Alain Hauser, Peter Bühlmann
From observational data alone, a causal DAG is only identifiable up to Markov equivalence. Interventional data generally improves identifiability; however, the gain of an intervent…
Statistical significance in high-dimensional linear models
Peter Bühlmann
We propose a method for constructing p-values for general hypotheses in a high-dimensional linear model. The hypotheses can be local for testing a single regression parameter or th…
Stable Graphical Model Estimation with Random Forests for Discrete, Continuous, and Mixed Variables
Bernd Fellinghauer, Peter Bühlmann, Martin Ryffel +2
A conditional independence graph is a concise representation of pairwise conditional independence among many variables. Graphical Random Forests (GRaFo) are a novel method for esti…
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
Alain Hauser, Peter Bühlmann
The investigation of directed acyclic graphs (DAGs) encoding the same Markov property, that is the same conditional independence relations of multivariate observational distributio…
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…