P. Bühlmann

发表

Methods for causal inference from gene perturbation experiments and validation

N. Meinshausen, Alain Hauser, J. Mooij, 2016, Proceedings of the National Academy of Sciences.

Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs

Alain Hauser, P. Bühlmann, 2013, 1303.3216.

Boosting With the L2 Loss

P. Bühlmann, Bin Yu, 2003 .

Boosting with the L2-loss: regression and classification

P. Bühlmann, Bin Yu, 2001 .

The group lasso for logistic regression

P. Bühlmann, L. Meier, Sara van de Geer, 2008 .

Consistent neighbourhood selection for sparse high-dimensional graphs with the Lasso

N. Meinshausen, P. Bühlmann, 2004 .

Comment to “ ` 1-Penalization for Mixture Regression Models ” by

P. Bühlmann, N. Städler, 2010 .

ℓ1-penalization for mixture regression models

S. Geer, P. Bühlmann, N. Städler, 2010, 1202.6046.

Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana

P. Bühlmann, E. Zitzler, L. Thiele, 2004, Genome Biology.

Variable Length Markov Chains: Methodology, Computing, and Software

P. Bühlmann, M. Mächler, 2004 .

Analyzing Bagging

P. Bühlmann, Bin Yu, 2001 .

Theory for the Lasso

S. Geer, P. Bühlmann, 2011 .

Score-based causal learning in additive noise models

P. Bühlmann, Christopher Nowzohour, 2013, 1311.6359.

Statistics for big data: A perspective

S. Geer, P. Bühlmann, 2018 .

Statistical Applications in Genetics and Molecular Biology Low-Order Conditional Independence Graphs for Inferring Genetic Networks

P. Bühlmann, A. Wille, Anja Wille, 2011 .

CAM: Causal Additive Models, high-dimensional order search and penalized regression pdf

Peter Bühlmann, Jonas Peters, Jan Ernest, 2013, ArXiv.

Sure independence screening for ultrahigh dimensional feature space Discussion

Jeffrey S. Morris, Gareth M. James, Hao Helen Zhang, 2008 .

Consistency for L₂boosting and matching pursuit with trees and tree-type basis functions

P. Bühlmann, 2002 .

Extreme events from the return-volume process: a discretization approach for complexity reduction

P. Bühlmann, 1998 .

Boosting for high-dimensional linear models

P. Bühlmann, 2006 .

Statistical significance in high-dimensional linear models

P. Bühlmann, 2013 .

Volatility estimation with functional gradient descent for very high-dimensional financial time series

P. Bühlmann, F. Audrino, 2003 .

How to use boosting for tumor classification with gene expression data

P. Bühlmann, M. Dettling, 2002 .

Decomposition and Model Selection for Large Contingency Tables

P. Bühlmann, M. Kalisch, Corinne Dahinden, 2009, Biometrical journal. Biometrische Zeitschrift.

Statistics for High-Dimensional Data

Sara van de Geer, Peter Bühlmann, S. Geer, 2011 .

Causal Stability Ranking

P. Bühlmann, M. Maathuis, L. Hennig, 2011 .

Predicting causal effects in large-scale systems from observational data

Peter Bühlmann, Diego Colombo, Markus Kalisch, 2010, Nature Methods.

Targeted Quantitative Analysis of Streptococcus pyogenes Virulence Factors by Multiple Reaction Monitoring*S

Nichole L. King, P. Bühlmann, R. Aebersold, 2008, Molecular & Cellular Proteomics.

Invariant Causal Prediction for Sequential Data

J. Peters, P. Bühlmann, Niklas Pfister, 2017, Journal of the American Statistical Association.

Estimation for High‐Dimensional Linear Mixed‐Effects Models Using ℓ1‐Penalization

P. Bühlmann, Jürg Schelldorfer, S. De Geer, 2010, 1002.3784.

Survival ensembles.

P. Bühlmann, T. Hothorn, M. J. van der Laan, 2006, Biostatistics.

Most Likely Transformations

Torsten Hothorn, Peter Buhlmann, P. Bühlmann, 2015, 1508.06749.

Invariance, Causality and Robustness 2018 Neyman Lecture ∗

P. Bühlmann, Peter B¨uhlmann, 2019 .

Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome

P. Bühlmann, P. Zimmermann, O. Laule, 2012, BMC Cancer.

Simultaneous analysis of large-scale RNAi screens for pathogen entry

Bernd Rinn, Pascale Cossart, Niko Beerenwinkel, 2014, BMC Genomics.

Comparison of Biclustering Methods : A Systematic Comparison and Evaluation of Biclustering Methods for Gene Expression Data

Lothar Thiele, Eckart Zitzler, Peter Bühlmann, 2006 .

DFG-SNF Research Group FOR 916 Statistical Regularization and Qualitative Constraints

S. Geer, P. Bühlmann, N. Städler, 2010 .

Missing values and sparse inverse covariance estimation

P. Bühlmann, N. Städler, 2009 .

MAXIMIN EFFECTS IN INHOMOGENEOUS LARGE-SCALE DATA BY NICOLAI MEINSHAUSEN

P. Bühlmann, 2015 .

Rejoinder: ℓ1-penalization for mixture regression models

S. Geer, P. Bühlmann, N. Städler, 2010 .

Causal Structure Learning and Inference: A Selective Review

P. Bühlmann, M. Kalisch, 2014 .

Marginal integration for fully robust causal inference

P. Bühlmann, J. Ernest, 2014 .

Bootstraps for Time Series

P. Bühlmann, 2002 .

Arabidopsis GERANYLGERANYL DIPHOSPHATE SYNTHASE 11 is a hub isozyme required for the production of most photosynthesis-related isoprenoids.

P. Bühlmann, E. Vranová, W. Gruissem, 2016, The New phytologist.

Sieve bootstrap for time series

P. Bühlmann, 1997 .

The blockwise bootstrap for general empirical processes of stationary sequences

P. Bühlmann, 1995 .

Two Optimal Strategies for Active Learning of Causal Models from Interventions

Alain Hauser, P. Bühlmann, 2012, ArXiv.

Bagging, subagging and bragging for improving some prediction algorithms

P. Bühlmann, 2003 .

A multi-marker association method for genome-wide association studies without the need for population structure correction

N. Meinshausen, P. Bühlmann, L. Meier, 2016, Nature Communications.

Block length selection in the bootstrap for time series

P. Bühlmann, H. Künsch, 1999 .

What is a linear process?

P Bühlmann, P. Bickel, P. Bühlmann, 1996, Proceedings of the National Academy of Sciences of the United States of America.

p-Values for High-Dimensional Regression

Peter Bühlmann, Nicolai Meinshausen, N. Meinshausen, 2008, 0811.2177.

An algorithm for nonparametric GARCH modelling

Peter Bühlmann, Alexander J. McNeil, P. Bühlmann, 2002 .

Robustified L 2 Boosting

Peter Bühlmann, Markus Kalisch, P. Bühlmann, 2007 .

Protein and gene model inference based on statistical modeling in k-partite graphs

Peter Bühlmann, Ermir Qeli, Christian H. Ahrens, 2010, Proceedings of the National Academy of Sciences.

Statistical Approach to Protein Quantification*

Ruedi Aebersold, Peter Bühlmann, Edward M Marcotte, 2013, Molecular & Cellular Proteomics.

Discovery of TNF inhibitors from a DNA-encoded chemical library based on diels-alder cycloaddition.

Peter Bühlmann, Dario Neri, Jörg Scheuermann, 2009, Chemistry & biology.

Sparse Contingency Tables and High-Dimensional Log-Linear Models for Alternative Splicing in Full-Length cDNA Libraries

P. Bühlmann, G. Parmigiani, Corinne Dahinden, 2006 .

Blockwise Bootstrapped Empirical Process for Stationary Sequences

P. Bühlmann, 1994 .

Network analysis of systems elements.

Eckart Zitzler, Peter Bühlmann, Wilhelm Gruissem, 2007, EXS.

Chapter 16 : Part II-Network Analysis of Systems Elements

P. Bühlmann, E. Zitzler, A. Wille, 2006 .

mboost Illustrations

P. Bühlmann, T. Hothorn, 2007 .

BOOSTING: A STATISTICAL PERSPECTIVE

P. Bühlmann, T. Hothorn, 2006 .

Conditional transformation models

Torsten Hothorn, Thomas Kneib, Peter Buhlmann, 2012, 1201.5786.

M L ] 2 F eb 2 00 9 High-Dimensional Additive Modeling

S. Geer, P. Bühlmann, L. Meier, 2009 .

M L ] 2 5 Ju n 20 08 High-Dimensional Additive Modeling

S. Geer, P. Bühlmann, L. Meier, 2009 .

Conjugate Direction Boosting

Peter Bühlmann, P. Bühlmann, R. W. Lutz, 2006 .

Modeling Inhomogeneous High-Dimensional Data-Sets: with Applications inLearning Large-Scale Gene Correlations

Peter Bühlmann, Juliane Schäfer, P. Bühlmann, 2007 .

Splines for financial volatility

P. Bühlmann, F. Audrino, 2007 .

GLMMLasso: An Algorithm for High-Dimensional Generalized Linear Mixed Models Using ℓ1-Penalization

Jürg Schelldorfer, Peter Bühlmann, P. Bühlmann, 2011, 1109.4003.

Toward causality and improving external validity

P. Bühlmann, 2020, Proceedings of the National Academy of Sciences.

Upper bounds for the number of true null hypotheses and novel estimates for error rates in multiple testing

N. Meinshausen, P. Bühlmann, 2004 .

Dynamic combination of models for nonlinear time series

P. Bühlmann, F. Ferrari, 2002 .

Dynamic adaptive partitioning for nonlinear time series

P. Bühlmann, 1999 .

2 Logistic Group Lasso 2 . 1 Model Setup

S. Geer, P. Bühlmann, L. Meier, 2007 .

Prediction of Spatial Cumulative Distribution Functions Using Subsampling: Comment

Hans R. Künsch, Peter Bühlmann, P. Bühlmann, 1999 .

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).

Discussion on ‘regularized regression for categorical data (Tutz and Gertheiss)’

P. Bühlmann, Ruben Dezeure, 2016 .

Variable length Markov chains: methodology, computing and software

Peter Bühlmann, Martin Mächler, P. Bühlmann, 2002 .

Sparse Boosting

P. Bühlmann, Bin Yu, 2006, J. Mach. Learn. Res..

Single-cell mapping of tumor heterogeneity in pediatric rhabdomyosarcoma reveals developmental signatures with therapeutic relevance

P. Bühlmann, H. Blau, B. Schäfer, 2022, bioRxiv.

Discussions of boosting papers, and rejoinders

Trevor Hastie, Peter J. Bickel, Robert Tibshirani, 2004 .

Systems-based analysis of Arabidopsis leaf growth reveals adaptation to water deficit

Mark Stitt, Pierre Hilson, Peter Bühlmann, 2012, Molecular systems biology.

Moving-average representation of autoregressive approximations

Peter Bühlmann, P. Bühlmann, 1995 .

Boosting Algorithms: with an Application to Bootstrapping Multivariate Time Series

Peter Bühlmann, Roman W. Lutz, P. Bühlmann, 2006 .

Lower bounds for the number of false null hypotheses for multiple testing of associations under general dependence structures

Peter Bühlmann, Nicolai Meinshausen, N. Meinshausen, 2005 .

On the role of additive regression for (high-dimensional) causal inference

Peter Bühlmann, Jan Ernest, P. Bühlmann, 2014 .

Mining Tissue Microarray Data to Uncover Combinations of Biomarker Expression Patterns that Improve Intermediate Staging and Grading of Clear Cell Renal Cell Cancer

Peter Bühlmann, Holger Moch, Peter Schraml, 2009, Clinical Cancer Research.

Twin Boosting: improved feature selection and prediction

Torsten Hothorn, Peter Bühlmann, P. Bühlmann, 2010, Stat. Comput..

Statistical approach to absolute protein quantification

P. Bühlmann, S. Gerster, 2011 .

Regularized Double Machine Learning in Partially Linear Models with Unobserved Confounding

P. Bühlmann, Corinne Emmenegger, 2021 .

Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm

P. Bühlmann, M. Kalisch, 2005, J. Mach. Learn. Res..

Boosting

P. Bühlmann, B. Yu, 2011 .

Statistical Analysis of Quantum Chemical Data Using Generalized XML/CML Archives for the Derivation of Molecular Design Rules

P. Bühlmann, H. Lüthi, M. Brändle, 2007 .

PENALIZED LIKELIHOOD AND BAYESIAN METHODS FOR SPARSE CONTINGENCY TABLES: AN ANALYSIS OF ALTERNATIVE SPLICING IN FULL-LENGTH cDNA LIBRARIES

Peter Bühlmann, Giovanni Parmigiani, Corinne Dahinden, 2006 .

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression

N. Meinshausen, P. Bühlmann, Domagoj Cevid, 2020, J. Mach. Learn. Res..

Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics

P. Bühlmann, Srećko Joksimović, Vitomir Kovanović, 2022, WIREs Data Mining Knowl. Discov..

On the Identifiability and Estimation of Causal Location-Scale Noise Models

Julia E. Vogt, B. Schölkopf, P. Bühlmann, 2022, ArXiv.

OPTIMALITY OF THE WESTFALL – YOUNG PERMUTATION PROCEDURE 3 see also Blanchard and Roquain [ 4 ] for FDR control under dependence

N. Meinshausen, P. Bühlmann, M. Maathuis, 2012 .

Hierarchical inference for genome-wide association studies

P. Bühlmann, M. Kalisch, Laura Buzdugan, 2020 .

Change-Point Detection for Graphical Models in the Presence of Missing Values pdf

Malte Londschien, Solt Kovács, Peter Bühlmann, 2019, J. Comput. Graph. Stat..

ST ] 4 N ov 2 01 6 Kernel-based Tests for Joint Independence

B. Schölkopf, J. Peters, P. Bühlmann, 2018 .

Hierarchical inference for genome-wide association studies: a view on methodology with software

P. Bühlmann, M. Kalisch, Laura Buzdugan, 2020, Computational Statistics.

High-dimensional variable screening and bias in subsequent inference, with an empirical comparison

P. Bühlmann, Jacopo Mandozzi, 2013, Computational Statistics.

Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome

P. Bühlmann, P. Zimmermann, W. Gruissem, 2012, BMC Cancer.

A systematic comparison and evaluation of biclustering methods for gene expression data

Lothar Thiele, Eckart Zitzler, Peter Bühlmann, 2006, Bioinform..

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

Bernhard Schölkopf, Peter Bühlmann, Sebastian Weichwald, 2018, J. Mach. Learn. Res..

groupICA: Independent component analysis for grouped data pdf

Bernhard Schölkopf, Peter Bühlmann, Sebastian Weichwald, 2018, ArXiv.

Causal Inference Using Graphical Models with the R Package pcalg

Peter Bühlmann, Diego Colombo, Marloes H. Maathuis, 2012 .

Causal stability ranking

Peter Bühlmann, Marloes H. Maathuis, Daniel J. Stekhoven, 2011, Bioinform..

High-Dimensional Statistics with a View Toward Applications in Biology

Peter Bühlmann, Markus Kalisch, Lukas Meier, 2014 .

DOUBLY DEBIASED LASSO: HIGH-DIMENSIONAL INFERENCE UNDER HIDDEN CONFOUNDING.

P. Bühlmann, Domagoj Cevid, Zijian Guo, 2020, Annals of statistics.

Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software

Laura Buzdugan, Peter Bühlmann, Markus Kalisch, 2020, Comput. Stat..

Hierarchical inference for genome-wide association studies: a view on methodology with software

Laura Buzdugan, Peter Bühlmann, Markus Kalisch, 2018, Computational Statistics.

Comments on: Data science, big data and statistics

Peter Bühlmann, P. Bühlmann, 2019, TEST.

High-dimensional simultaneous inference with the bootstrap

Peter Bühlmann, Cun-Hui Zhang, Cun-Hui Zhang, 2016, 1606.03940.

Comments on: A random forest guided tour

Peter Bühlmann, Florencia Leonardi, F. Leonardi, 2016 .

Confidence Intervals and Tests for High-Dimensional Models: A Compact Review

Peter Bühlmann, P. Bühlmann, 2015 .

High-dimensional variable screening and bias in subsequent inference, with an empirical comparison

Peter Bühlmann, Jacopo Mandozzi, P. Bühlmann, 2013, Computational Statistics.

Partial Least Squares for Heterogeneous Data

P. Bühlmann, 2014 .

Discussion of “The Evolution of Boosting Algorithms” and “Extending Statistical Boosting”

A Ziegler, Z Wang, J Gertheiss, 2014, Methods of Information in Medicine.

Hierarchical Testing in the High-Dimensional Setting With Correlated Variables

Peter Bühlmann, Jacopo Mandozzi, P. Bühlmann, 2013, 1312.5556.

Controlling false positive selections in high-dimensional regression and causal inference

Peter Bühlmann, Markus Kalisch, Philipp Rütimann, 2013, Statistical methods in medical research.

MissForest - non-parametric missing value imputation for mixed-type data

Peter Bühlmann, Daniel J. Stekhoven, P. Bühlmann, 2011, Bioinform..

Single-cell profiling of alveolar rhabdomyosarcoma reveals RAS pathway inhibitors as cell-fate hijackers with therapeutic relevance

P. Bühlmann, H. Blau, D. Surdez, 2023, Science advances.

Gene Expression Signatures Identify Rhabdomyosarcoma Subtypes and Detect a Novel t(2;2)(q35;p23) Translocation Fusing PAX3 to NCOA1

P. Bühlmann, M. Dettling, B. Schäfer, 2004, Cancer Research.

High-dimensional statistics, with applications to genome-wide association studies

Peter Bühlmann, P. Bühlmann, 2017 .

Sieve Bootstrap With Variable-Length Markov Chains for Stationary Categorical Time Series

Peter Bühlmann, P. Bühlmann, 2002 .

Analyzing gene expression data in terms of gene sets: methodological issues

Peter Bühlmann, Jelle J. Goeman, P. Bühlmann, 2007, Bioinform..

Supervised clustering of genes

Peter Bühlmann, Marcel Dettling, P. Bühlmann, 2002, Genome Biology.

Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020)

Peter Buhlmann, Solt Kov'acs, Housen Li, 2020, Journal of the Korean Statistical Society.

Boosting for Tumor Classification with Gene Expression Data

Peter Bühlmann, Marcel Dettling, P. Bühlmann, 2003, Bioinform..

Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software

P. Bühlmann, M. Kalisch, Laura Buzdugan, 2020, Computational Statistics.

Domain adaptation under structural causal models

P. Bühlmann, Yuansi Chen, 2020, J. Mach. Learn. Res..

Finding predictive gene groups from microarray data

Peter Bühlmann, Marcel Dettling, P. Bühlmann, 2004 .

A Fast Non-parametric Approach for Causal Structure Learning in Polytrees

Armeen Taeb, Peter Buhlmann, Mona Azadkia, 2021 .

Robustification of the PC-Algorithm for Directed Acyclic Graphs

Peter Bühlmann, Markus Kalisch, P. Bühlmann, 2008 .

High-dimensional simultaneous inference with the bootstrap

P. Bühlmann, Ruben Dezeure, Ruben Dezeure, 2017, TEST.

High-dimensional learning of linear causal networks via inverse covariance estimation

Po-Ling Loh, Peter Bühlmann, Po-Ling Loh, 2013, J. Mach. Learn. Res..

Multi-Omic Profiling of the Liver Across Diets and Age in a Diverse Mouse Population AUTHOR LIST

Robert W. Williams, Evan G. Williams, P. Bühlmann, 2021 .

Gene expression profiles and risk stratification in childhood acute lymphoblastic leukemia

P. Bühlmann, M. Dettling, B. Schäfer, 2004 .

Causal statistical inference in high dimensions

Peter Bühlmann, P. Bühlmann, 2013, Math. Methods Oper. Res..

Supervised gene clustering with penalized logistic regression

Peter Bühlmann, Marcel Dettling, P. Bühlmann, 2003 .

SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research

Gunnar Rätsch, Karsten Borgwardt, Peter Bühlmann, 2020, MIE.

Model-based boosting in high dimensions

Torsten Hothorn, Peter Bühlmann, P. Bühlmann, 2006, Bioinform..

Sieve bootstrap for smoothing in nonstationary time series

Peter Bühlmann, P. Bühlmann, 1998 .

Weak dependence beyond mixing and asymptotics for nonparametric regression

P. Bühlmann, P. Doukhan, P. A. Nze, 2002 .

The blockwise bootstrap for general parameters of a strationary time series

Peter Bühlmann, P. Bühlmann, H. Künsch, 1993 .

Bagging, Boosting and Ensemble Methods

Peter Bühlmann, P. Bühlmann, 2012 .

A Look at Robustness and Stability of 1-versus 0-Regularization : Discussion of Papers by Bertsimas et al . and Hastie et al .

P. Bühlmann, Yuansi Chen, A. Taeb, 2020 .

Annotating novel genes by integrating synthetic lethals and genomic information

Peter Bühlmann, Wilhelm Gruissem, Markus Kalisch, 2008, BMC Systems Biology.

Using synthetic lethality and integration of genomic data for finding spindle migration genes

P. Bühlmann, M. Kalisch, L. Meier, 2006 .

MAXIMUM LIKELIHOOD FOR SPARSE DIRECTED ACYCLIC GRAPHS

P. Bühlmann, DE Saravan, P. E. B. Ühlmann, 2013 .

Selection of Credibility Regression Models

P. Bühlmann, H. Bühlmann, 1999, ASTIN Bulletin.

Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract) pdf

Peter Bühlmann, Alain Hauser, Alain Hauser, 2011, UAI.

Model-based Boosting 2.0

Torsten Hothorn, Benjamin Hofner, Matthias Schmid, 2010, J. Mach. Learn. Res..

Prediction and variable selection with the adaptive Lasso

Sara van de Geer, Peter Bühlmann, Shuheng Zhou, 2010 .

Distributional anchor regression

P. Bühlmann, Beate Sick, Lucas Kook, 2021, Statistics and Computing.

Invariance, Causality and Robustness.

Peter Bühlmann, P. Bühlmann, 2018 .

Invariance, Causality and Robustness

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

EVE (external variance estimation) increases statistical power for detecting differentially expressed genes.

Peter Bühlmann, Wilhelm Gruissem, Lars Hennig, 2007, The Plant journal : for cell and molecular biology.

Missing values: sparse inverse covariance estimation and an extension to sparse regression

Peter Bühlmann, Nicolas Städler, P. Bühlmann, 2009, Statistics and Computing.

Pattern alternating maximization algorithm for missing data in high-dimensional problems

Peter Bühlmann, Daniel J. Stekhoven, Nicolas Städler, 2014, J. Mach. Learn. Res..

Structure Learning with Bow-free Acyclic Path Diagrams

Peter Bühlmann, Marloes H. Maathuis, Christopher Nowzohour, 2015 .

Efficient and adaptive post-model-selection estimators

Peter Bühlmann, P. Bühlmann, 1999 .

Robustified L2 boosting

Peter Bühlmann, Markus Kalisch, Roman Werner Lutz, 2008, Comput. Stat. Data Anal..

A new mixing notion and functional central limit theorems for a sieve bootstrap in time series

Peter J. Bickel, Peter Bühlmann, P. Bickel, 1999 .

Closure of Linear Processes

P. Bickel, P. Bühlmann, 1997 .

Missing values: sparse inverse covariance estimation and an extension to sparse regression

P. Bühlmann, N. Städler, 2012, Stat. Comput..

Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables

Peter Bühlmann, Bernd Fellinghauer, Martin Ryffel, 2011, Comput. Stat. Data Anal..

Tree structured GARCH models

P. Bühlmann, F. Audrino, 2000 .

Structural Intervention Distance for Evaluating Causal Graphs

Peter Bühlmann, Jonas Peters, J. Peters, 2015, Neural Computation.

Two optimal strategies for active learning of causal models from interventional data

Peter Bühlmann, Alain Hauser, Alain Hauser, 2012, Int. J. Approx. Reason..

On the Identifiability and Estimation of Causal Location-Scale Noise Models

Julia E. Vogt, B. Schölkopf, P. Bühlmann, 2022, ICML.

ricu: R’s interface to intensive care data

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

Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements

Peter Buhlmann, Corinne Emmenegger, P. Bühlmann, 2021 .

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.

Causal inference in high dimensions II

Peter Bühlmann, P. Bühlmann, 2012 .

Rejoinder on: High-dimensional simultaneous inference with the bootstrap

Cun-Hui Zhang, P. Bühlmann, Ruben Dezeure, 2017 .

Rejoinder on: High-dimensional simultaneous inference with the bootstrap

P. Bühlmann, Ruben Dezeure, Cun-Hui Zhang, 2017, Test (Madrid).

A Sequential Rejection Testing Method for High-Dimensional Regression with Correlated Variables

Peter Bühlmann, Jacopo Mandozzi, P. Bühlmann, 2015, The international journal of biostatistics.

The Weighted Generalised Covariance Measure

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

Variable Length Markov Chains: Methodology, Computing, and Software

Peter Bühlmann, Martin Mächler, P. Bühlmann, 2004 .

Model Selection for Variable Length Markov Chains and Tuning the Context Algorithm

Peter Bühlmann, P. Bühlmann, 2000 .

Tree‐structured generalized autoregressive conditional heteroscedastic models

Peter Bühlmann, Francesco Audrino, P. Bühlmann, 2001 .

Plug‐in Machine Learning for Partially Linear Mixed‐Effects Models with Repeated Measurements

P. Bühlmann, Corinne Emmenegger, 2021, Scandinavian Journal of Statistics.

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

Peter Buhlmann, Christoph Schultheiss, Ming Yuan, 2021 .

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

P. Bühlmann, Zijian Guo, 2022 .

Identifying cancer pathway dysregulations using differential causal effects

P. Bühlmann, N. Beerenwinkel, Domagoj Cevid, 2021, bioRxiv.

Structure Learning for Directed Trees

Rajen D. Shah, J. Peters, P. Bühlmann, 2021, J. Mach. Learn. Res..

Perturbations and Causality in Gaussian Models

Armeen Taeb, Peter Buhlmann, P. Bühlmann, 2021 .

The Molecular Landscape of the Aging Mouse Liver

Robert W. Williams, Evan G. Williams, P. Bühlmann, 2020, bioRxiv.

Assessing the goodness of fit of linear regression via higher-order least squares

P. Bühlmann, Ming Yuan, C. Schultheiss, 2021 .

Hypersurfaces and Their Singularities in Partial Correlation Testing

Bernd Sturmfels, Caroline Uhler, Peter Bühlmann, 2012, Found. Comput. Math..

Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data

Peter Bühlmann, Nicolai Meinshausen, N. Meinshausen, 2014, Proceedings of the IEEE.

Group inference in high dimensions with applications to hierarchical testing

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

Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries

Peter Bühlmann, Giovanni Parmigiani, Corinne Dahinden, 2007, BMC Bioinformatics.

Absolute Risk: Methods and Applications in Clinical Management and Public Health

Mitchell H. Gail, Ruth M. Pfeiffer, P. Bühlmann, 2018 .

High-dimensional Covariance Estimation Based On Gaussian Graphical Models

Min Xu, Peter Bühlmann, Shuheng Zhou, 2010, J. Mach. Learn. Res..

The blockwise bootstrap in time series and empirical processes

Peter Lukas Bühlmann, P. Bühlmann, 1993 .

Understanding human functioning using graphical models

Peter Bühlmann, Ulrich Mansmann, Eva Grill, 2010, BMC medical research methodology.

Predicting sepsis using deep learning across international sites: a retrospective development and validation study

Karsten M. Borgwardt, N. Meinshausen, P. Bühlmann, 2023, EClinicalMedicine.

Mining Tissue Microarray Data to Uncover Combinations of Biomarker Expression Patterns that Improve Intermediate Staging and Grading of Clear Cell Renal Cell Cancer

P. Bühlmann, G. Kristiansen, H. Moch, 2009, Clinical Cancer Research.

Assessing statistical significance in multivariable genome wide association analysis

Arcadi Navarro, Laura Buzdugan, Peter Bühlmann, 2016, Bioinform..

Systems-based analysis of Arabidopsis leaf growth reveals adaptation to water deficit

P. Bühlmann, L. Hennig, W. Gruissem, 2012 .

Deconfounding and Causal Regularization for Stability and External Validity

Peter Buhlmann, Domagoj 'Cevid, P. Bühlmann, 2020, 2008.06234.

Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs

Alain Hauser, P. Bühlmann, 2011, J. Mach. Learn. Res..

Methods for causal inference from gene perturbation experiments and validation

N. Meinshausen, Alain Hauser, J. Peters, 2016, Proceedings of the National Academy of Sciences.

Boosting and l1-Penalty Methods for High-dimensional Data with Some Applications in Genomics

Peter Bühlmann, P. Bühlmann, 2005, GfKl.

Boosting Algorithms: Regularization, Prediction and Model Fitting. Rejoinder.

Torsten Hothorn, Peter Bühlmann, P. Bühlmann, 2007 .

Boosting, model selection, lasso and nonnegative garrote

Peter Bühlmann, Bin Yu, P. Bühlmann, 2005 .

One Modern Culture of Statistics: Comments on Statistical Modeling: The Two Cultures (Breiman, 2001b)

P. Bühlmann, 2021, Observational Studies.

Rejoinder: Invariance, Causality and Robustness

P. Bühlmann, 2020, Statistical Science.

Rejoinder: Invariance, Causality and Robustness (2018 Neyman Lecture)

P. Bühlmann, Stefan Wager, 2020 .

Synchronizing multivariate financial time series

Peter Bühlmann, Francesco Audrino, P. Bühlmann, 2004 .

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

P. Bühlmann, Niklas Pfister, S. Weichwald, 2018, J. Mach. Learn. Res..

Mathematics, Statistics and Data Science

Andrew M. Stuart, Peter Bühlmann, P. Bühlmann, 2016 .

LOCALLY ADAPTIVE LAG‐WINDOW SPECTRAL ESTIMATION

P. Bühlmann, 1996 .

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

P. Bühlmann, A. Munk, Housen Li, 2020, ArXiv.

Some Themes in High-Dimensional Statistics

Marina Vannucci, Peter Bühlmann, Sylvia Richardson, 2016 .

A systematic comparison and evaluation of biclustering methods for gene expression data

P. Bühlmann, E. Zitzler, L. Thiele, 2006, Bioinform..