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
73 papers
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…
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…
Separating internal and externally-forced contributions to global temperature variability using a Bayesian stochastic energy balance framework
Maybritt Schillinger, Beatrice Ellerhoff, Robert Scheichl +1
Earth's temperature variability can be partitioned into internal and externally-forced components. Yet, underlying mechanisms and their relative contributions remain insufficiently…
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…
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…
Reconstruction of the neutrino mass as a function of redshift
Christiane S. Lorenz, Lena Funcke, Matthias Löffler +1
We reconstruct the neutrino mass as a function of redshift, z, from current cosmological data using both standard binned priors and linear spline priors with variable knots. Using…
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.…
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…
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…
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…
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…
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…
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach
Niklas Pfister, Stefan Bauer, Jonas Peters
Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference…
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…
Particle filters for high-dimensional geoscience applications: a review
Peter Jan van Leeuwen, Hans R. Künsch, Lars Nerger +2
Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, but their application to the geosciences has been limit…
Nash equilibrium for risk-averse investors in a market impact game with transient price impact
Xiangge Luo, Alexander Schied
We consider a market impact game for risk-averse agents that are competing in a market model with linear transient price impact and additional transaction costs. For both finit…
Hierarchical inference for genome-wide association studies: a view on methodology with software
Claude Renaux, Laura Buzdugan, Markus Kalisch +1
We provide a view on high-dimensional statistical inference for genome-wide association studies (GWAS). It is in part a review but covers also new developments for meta analysis wi…
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…