739 citations · 1.8k across the 34 of their papers we have counts for
9 papers · 1 filter
Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets
Malte Londschien, Peter Bühlmann
We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condi…
Distributional Robustness and Transfer Learning Through Empirical Bayes
Michael Law, Peter Bühlmann, Ya'acov Ritov
We consider the problem of statistical inference on parameters of a target population when auxiliary observations are available from related populations. We propose a flexible empi…
A Fast Non-parametric Approach for Local Causal Structure Learning
Mona Azadkia, Armeen Taeb, Peter Bühlmann
We study the problem of causal structure learning with essentially no assumptions on the functional relationships and noise. We develop DAG-FOCI, a computationally fast algorithm f…
Double-estimation-friendly inference for high-dimensional misspecified models
Rajen D. Shah, Peter Bühlmann
All models may be wrong -- but that is not necessarily a problem for inference. Consider the standard -test for the significance of a variable for predicting response wh…
Kernel-based Tests for Joint Independence
Niklas Pfister, Peter Bühlmann, Bernhard Schölkopf +1
We investigate the problem of testing whether random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method builds on ideas of the two…
Geometry of the faithfulness assumption in causal inference
Caroline Uhler, Garvesh Raskutti, Peter Bühlmann +1
Many algorithms for inferring causality rely heavily on the faithfulness assumption. The main justification for imposing this assumption is that the set of unfaithful distributions…
Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling
Shuheng Zhou, Sara van de Geer, Peter Bühlmann
We show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent for high-dimensional model selection in linear and Gaussian graphical models. Our conditions for consi…
Discussion: One-step sparse estimates in nonconcave penalized likelihood models
Peter Bühlmann, Lukas Meier
Discussion of ``One-step sparse estimates in nonconcave penalized likelihood models'' [arXiv:0808.1012]
Smoothing -penalized estimators for high-dimensional time-course data
Lukas Meier, Peter Bühlmann
When a series of (related) linear models has to be estimated it is often appropriate to combine the different data-sets to construct more efficient estimators. We use -pena…