Peter Buhlmann

发表

Estimating high-dimensional intervention effects from observational data

M. Maathuis, M. Kalisch, Peter Buhlmann, 2008, 0810.4214.

On the conditions used to prove oracle results for the Lasso

S. Geer, Peter Buhlmann, 2009, 0910.0722.

High-dimensional graphs and variable selection with the Lasso

N. Meinshausen, Peter Buhlmann, 2006, math/0608017.

On asymptotically optimal confidence regions and tests for high-dimensional models

S. Geer, Y. Ritov, Peter Buhlmann, 2013, 1303.0518.

BOOSTING ALGORITHMS: REGULARIZATION, PREDICTION AND MODEL FITTING

Peter Buhlmann, T. Hothorn, 2007, 0804.2752.

High-dimensional additive modeling

S. Geer, Peter Buhlmann, L. Meier, 2008, 0806.4115.

Maximin effects in inhomogeneous large-scale data

N. Meinshausen, Peter Buhlmann, 2014, 1406.0596.

Boosting for high-dimensional linear models

Peter Buhlmann, 2006, math/0606789.

Geometry of the faithfulness assumption in causal inference

Peter Buhlmann, B. Yu, G. Raskutti, 2012, 1207.0547.

Structural Intervention Distance (SID) for Evaluating Causal Graphs

J. Peters, Peter Buhlmann, 2013, 1306.1043.

Identifiability of Gaussian structural equation models with equal error variances

J. Peters, Peter Buhlmann, 2012, 1205.2536.

Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm

M. Maathuis, M. Kalisch, Peter Buhlmann, 2009, 0906.3204.

Statistical significance in high-dimensional linear models

Peter Buhlmann, 2012, 1202.1377.

$\ell_0$-penalized maximum likelihood for sparse directed acyclic graphs

S. Geer, Peter Buhlmann, 2012, 1205.5473.

The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso)

S. Geer, Peter Buhlmann, Shuheng Zhou, 2011 .

Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling

S. Geer, Peter Buhlmann, Shuheng Zhou, 2009, 0903.2515.

S. Geer, Cun-Hui Zhang, Peter Buhlmann, 2012, 1209.5908.

Boosting Methods: Why They Can Be Us eful for High-Dimensional Data

Peter Buhlmann, 2003 .

Kernel‐based tests for joint independence

B. Schölkopf, J. Peters, Peter Buhlmann, 2016, 1603.00285.

Anchor regression: Heterogeneous data meet causality

N. Meinshausen, J. Peters, Peter Buhlmann, 2018, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Confidence Intervals for Maximin Effects in Inhomogeneous Large-Scale Data

N. Meinshausen, Peter Buhlmann, Dominik Rothenhausler, 2015, 1502.07963.

High-Dimensional Inference: Confidence Intervals, $p$-Values and R-Software hdi

N. Meinshausen, Peter Buhlmann, Ruben Dezeure, 2014, 1408.4026.

Spectral Deconfounding via Perturbed Sparse Linear Models

N. Meinshausen, Peter Buhlmann, Domagoj Cevid, 2018, 1811.05352.

Invariance, Causality and Robustness

P. Bühlmann, Peter Buhlmann, 2018, Statistical Science.

Pattern Alternating Maximization Algorithm for High-Dimensional Missing Data

Peter Buhlmann, P. Buehlmann, D. Stekhoven, 2010 .

Causal inference in partially linear structural equation models

Peter Buhlmann, J. Ernest, Dominik Rothenhausler, 2016, The Annals of Statistics.

Marginal integration for nonparametric causal inference

Peter Buhlmann, J. Ernest, 2014, 1405.1868.

Estimating heterogeneous treatment effects in nonstationary time series with state-space models

Peter Buhlmann, Shu Li, 2018, 1812.04063.

Selection of Carbonic Anhydrase IX Inhibitors from One Million DNA-Encoded Compounds.

Markus Kalisch, Dario Neri, Peter Buhlmann, 2011, ACS chemical biology.

Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables

Peter Buhlmann, Zijian Guo, 2022, 2203.12808.

Stabilizing variable selection and regression

Evan G. Williams, J. Peters, Peter Buhlmann, 2019, The Annals of Applied Statistics.

Splines for Financial Volatility

Peter Buhlmann, F. Audrino, 2007 .

Discussion: "A significance test for the lasso"

S. Geer, Peter Buhlmann, L. Meier, 2014, 1405.6792.

Random Forests for Change Point Detection

Peter Buhlmann, Malte Londschien, Solt Kov'acs, 2022, 2205.04997.

Double-Estimation-Friendly Inference for High-Dimensional Misspecified Models

Rajen Dinesh Shah, Peter Buhlmann, 2019, Statistical Science.

Pattern Alternating Maximization Algorithm for Missing Data in Large P, Small N Problems

Peter Buhlmann, D. Stekhoven, Nicolas Stadler, 2010, 1005.0366.

Treatment Effect Estimation from Observational Network Data using Augmented Inverse Probability Weighting and Machine Learning

Peter Buhlmann, Corinne Emmenegger, Meta-Lina Spohn, 2022, 2206.14591.

The Weighted Generalised Covariance Measure

P. Bühlmann, Peter Buhlmann, Julia Hörrmann, 2021, J. Mach. Learn. Res..

Boosting for high-dimensional linear models

Peter Buhlmann, 2006, math/0606789.

Invariance, Causality and Robustness

Peter Buhlmann, 2018, 1812.08233.

High-dimensional graphs and variable selection with the Lasso

N. Meinshausen, Peter Buhlmann, 2006, math/0608017.

Discussion: One-step sparse estimates in nonconcave penalized likelihood models

Peter Buhlmann, L. Meier, 2008, 0808.1013.

Distributionally robust and generalizable inference

Peter Buhlmann, Dominik Rothenhausler, 2022, 2209.09352.

Treatment Effect Estimation from Observational Network Data using Augmented Inverse Probability Weighting and Machine Learning

Peter Buhlmann, Timon Elmer, Corinne Emmenegger, 2022, 2206.14591.

Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries

Peter Buhlmann, A. Munk, Housen Li, 2020, 2010.10194.

On the pitfalls of Gaussian likelihood scoring for causal discovery

Peter Buhlmann, Christoph Schultheiss, 2022, 2210.11104.

Higher-order least squares: assessing partial goodness of fit of linear causal models

P. Bühlmann, Peter Buhlmann, C. Schultheiß, 2021, Journal of the American Statistical Association.

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions

Juan L Gamella, Peter Buhlmann, C. Heinze-Deml, 2022, 2211.14897.

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions

Juan L Gamella, Peter Buhlmann, C. Heinze-Deml, 2022, 2211.14897.

Discussion of Big Bayes Stories and BayesBag

Peter Buhlmann, 2014, 1405.4977.

Regularizing double machine learning in partially linear endogenous models

Peter Buhlmann, Corinne Emmenegger, Peter Buhlmann, 2021, Electronic Journal of Statistics.

Goodness‐of‐fit testing in high dimensional generalized linear models

Rajen Dinesh Shah, Rajen D. Shah, Peter Buhlmann, 2019, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Goodness‐of‐fit tests for high dimensional linear models

Rajen Dinesh Shah, Rajen D. Shah, Peter Buhlmann, 2015, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Computationally efficient change point detection for high-dimensional regression

Peter Buhlmann, Florencia Leonardi, F. Leonardi, 2016, 1601.03704.

Causal inference by using invariant prediction: identification and confidence intervals

Jonas Peters, Nicolai Meinshausen, Peter Buhlmann, 2015, 1501.01332.

REJOINDER: BOOSTING ALGORITHMS: REGULARIZATION, PREDICTION AND MODEL FITTING

Torsten Hothorn, Peter Buhlmann, Peter Buhlmann, 2007, 0804.2777.

Asymptotic optimality of the Westfall--Young permutation procedure for multiple testing under dependence

Nicolai Meinshausen, Marloes H. Maathuis, Peter Buhlmann, 2011, 1106.2068.

Multicarving for high-dimensional post-selection inference

Peter Buhlmann, Christoph Schultheiss, Claude Renaux, 2020, 2006.04613.

High-dimensional inference in misspecified linear models

Sara van de Geer, Peter Buhlmann, S. Geer, 2015, 1503.06426.

A Fast Non-parametric Approach for Local Causal Structure Learning

Peter Buhlmann, A. Taeb, Mona Azadkia, 2021, 2111.14969.

Seeded Binary Segmentation: A general methodology for fast and optimal changepoint detection

Peter Buhlmann, A. Munk, Housen Li, 2020, Biometrika.

Confidence and Uncertainty Assessment for Distributional Random Forests

N. Meinshausen, Peter Buhlmann, Corinne Emmenegger, 2023, 2302.05761.

Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions

Nicolai Meinshausen, Peter Buhlmann, Dominik Rothenhausler, 2017, The Annals of Statistics.

Stabilizing variable selection and regression

Ruedi Aebersold, Jonas Peters, Peter Buhlmann, 2019, The Annals of Applied Statistics.

Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams

Robin J. Evans, Marloes H. Maathuis, Christopher Nowzohour, 2015, 1508.01717.

ricu: R’s interface to intensive care data

Nicolai Meinshausen, Peter Buhlmann, Nicolas Bennett, 2021, GigaScience.

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning pdf

Karsten M. Borgwardt, Michael Moor, Karsten Borgwardt, 2021, ArXiv.

Group inference in high dimensions with applications to hierarchical testing

P. Bühlmann, Peter Buhlmann, T. Cai, 2019, Electronic Journal of Statistics.

TSCI: two stage curvature identification for causal inference with invalid instruments

Peter Buhlmann, Zijian Guo, Corinne Emmenegger, 2023, 2304.00513.

Causal Inference with Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning

Peter Buhlmann, Zijian Guo, 2022, 2203.12808.

Causality-oriented robustness: exploiting general additive interventions

Peter Buhlmann, Xinwei Shen, Armeen Taeb, 2023, ArXiv.

Perturbations and Causality in Gaussian Latent Variable Models

Juan L Gamella, Peter Buhlmann, C. Heinze-Deml, 2021, 2101.06950.

Ancestor regression in linear structural equation models

Peter Buhlmann, C. Schultheiss, 2022, Biometrika.

Heavy-dense QCD at fixed baryon number without a sign problem

Peter Buhlmann, U. Wenger, 2021, Proceedings of The 38th International Symposium on Lattice Field Theory — PoS(LATTICE2021).

Smoothing ℓ1-penalized estimators for high-dimensional time-course data

Peter Buhlmann, Peter Buhlmann, L. Meier, 2007, 0712.1654.

Random Forests for Change Point Detection

Peter Buhlmann, Malte Londschien, S. Kov'acs, 2022, J. Mach. Learn. Res..

Treatment Effect Estimation with Observational Network Data using Machine Learning

Peter Buhlmann, Timon Elmer, Corinne Emmenegger, 2022, 2206.14591.

Graphical Elastic Net and Target Matrices: Fast Algorithms and Software for Sparse Precision Matrix Estimation

Peter Buhlmann, Solt Kov'acs, Tobias Ruckstuhl, 2021, 2101.02148.

Confidence and Uncertainty Assessment for Distributional Random Forests

N. Meinshausen, Peter Buhlmann, Corinne Emmenegger, 2023, 2302.05761.

Distributionally Robust and Generalizable Inference

Peter Buhlmann, Dominik Rothenhausler, 2022, Statistical Science.

High-dimensional additive modeling

S. Geer, Peter Buhlmann, L. Meier, 2008, 0806.4115.

Spectral Deconfounding and Perturbed Sparse Linear Models

N. Meinshausen, Peter Buhlmann, Domagoj Cevid, 2018, 1811.05352.

Goodness‐of‐fit testing in high dimensional generalized linear models

Rajen Dinesh Shah, P. Bühlmann, Peter Buhlmann, 2019, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Selection of Carbonic Anhydrase IX Inhibitors from One Million DNA-Encoded Compounds.

M. Kalisch, Peter Buhlmann, C. Supuran, 2011, ACS chemical biology.

Multicarving for high-dimensional post-selection inference

Peter Buhlmann, Claude Renaux, Christoph Schultheiss, 2020, Electronic Journal of Statistics.

High dimensional sparse covariance estimation via directed acyclic graphs

Peter Buhlmann, Peter Buhlmann, Philipp Rutimann, 2009, 0911.2375.

Learning and scoring Gaussian latent variable causal models with unknown additive interventions

Peter Buhlmann, C. Heinze-Deml, Armeen Taeb, 2021, 2101.06950.

Nonparametric causal inference from observational time series through marginal integration

Shu Li, Peter Buhlmann, Jan Ernest, 2016, 1606.04431.

Efficient Multiple Testing Adjustment for Hierarchical Inference

Peter Buhlmann, Claude Renaux, Peter Buhlmann, 2021, 2104.15028.

DISCUSSION OF: TREELETS—AN ADAPTIVE MULTI-SCALE BASIS FOR SPARSE UNORDERED DATA

Nicolai Meinshausen, Peter Buhlmann, N. Meinshausen, 2008, 0807.4018.