Peter Bühlmann

发表

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

Peter Bühlmann, P. Bühlmann, 2005, GfKl.

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

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

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

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

High-Dimensional Statistics with a View Toward Applications in Biology

Peter Bühlmann, Markus Kalisch, Lukas Meier, 2014 .

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

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

Twin Boosting: improved feature selection and prediction

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

Statistics for High-Dimensional Data

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

Invariance, Causality and Robustness.

Peter Bühlmann, P. Bühlmann, 2018 .

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

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

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

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

Boosting and greedy algorithms

Sara van de Geer, Peter Bühlmann, 2011 .

Domain adaptation under structural causal models pdf

Peter Bühlmann, Yuansi Chen, 2020, ArXiv.

P-values for linear models and beyond

Sara van de Geer, Peter Bühlmann, 2011 .

Some Themes in High-Dimensional Statistics

Marina Vannucci, Peter Bühlmann, Sylvia Richardson, 2016 .

Causal stability ranking

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

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

Peter Bühlmann, Axel Munk, Housen Li, 2020, ArXiv.

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

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

Causal Inference Using Graphical Models with the R Package pcalg

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

Discoveries at risk

Peter Bühlmann, Nicolai Meinshausen, 2003 .

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

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

High-dimensional Covariance Estimation Based On Gaussian Graphical Models

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

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

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

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

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

Assessing statistical significance in multivariable genome wide association analysis

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

Boosting Algorithms: with an Application to Bootstrapping Multivariate Time Series

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

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

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

Lasso for linear models

Sara van de Geer, Peter Bühlmann, 2011 .

Dynamic adaptive partitioning for nonlinear time series

Peter Bühlmann, 1998 .

Variable Length Markov Chains: Methodology, Computing, and Software

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

Boosting for Tumor Classification with Gene Expression Data

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

Ensemble Methods of Computational Inference

Torsten Hothorn, Peter Bühlmann, Marcel Dettling, 2005 .

Comments on: Data science, big data and statistics

Peter Bühlmann, P. Bühlmann, 2019, TEST.

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

Peter Bühlmann, P. Bühlmann, 2017 .

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

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

Robustified L2 boosting

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

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

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

Model-based boosting in high dimensions

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

Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data

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

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

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

Assigning statistical significance in high-dimensional problems

Peter Bühlmann, 2012 .

Comments on: A random forest guided tour

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

Hypersurfaces and Their Singularities in Partial Correlation Testing

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

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

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

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

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

Non-convex loss functions and ℓ 1 -regularization

Sara van de Geer, Peter Bühlmann, 2011 .

Two Optimal Strategies for Active Learning of Causal Models from Interventions pdf

Peter Bühlmann, Alain Hauser, 2012, ArXiv.

Causal inference in high dimensions II

Peter Bühlmann, P. Bühlmann, 2012 .

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

Peter Bühlmann, P. Bühlmann, 2015 .

Understanding human functioning using graphical models

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

Model-based Boosting 2.0

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

Annotating novel genes by integrating synthetic lethals and genomic information

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

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

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

Boosting, model selection, lasso and nonnegative garrote

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

groupICA: Independent component analysis for grouped data pdf

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

Causal statistical inference in high dimensions

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

Predicting causal effects in large-scale systems from observational data

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

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 .

Generalized linear models and the Lasso

Sara van de Geer, Peter Bühlmann, 2011 .

Additive models and many smooth univariate functions

Sara van de Geer, Peter Bühlmann, 2011 .

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

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

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

Peter Bühlmann, Markus Kalisch, 2007, J. Mach. Learn. Res..

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression pdf

Peter Bühlmann, Nicolai Meinshausen, Domagoj Cevid, 2020, ArXiv.

Structural Intervention Distance for Evaluating Causal Graphs

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

Bagging, Boosting and Ensemble Methods

Peter Bühlmann, P. Bühlmann, 2012 .

Structure Learning for Directed Trees pdf

Peter Bühlmann, Jonas Peters, Martin Emil Jakobsen, 2021, ArXiv.

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.

Supervised clustering of genes

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

High-dimensional simultaneous inference with the bootstrap

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

Model selection for variable length Markov chains and tuning the context algorithm

Peter Bühlmann, 1997 .

p-Values for High-Dimensional Regression

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

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

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

Discussion on: Sparse regression: Scalable algorithms and empirical performance & Best Subset, Forward Stepwise, or Lasso? Analysis and recommendations based on extensive comparisons

Armeen Taeb, Peter Bühlmann, Yuansi Chen, 2020 .

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

Lothar Thiele, Eckart Zitzler, Peter Bühlmann, 2006 .

Sieve bootstrap for smoothing in nonstationary time series

Peter Bühlmann, P. Bühlmann, 1998 .

Block length selection in the bootstrap for time series

Peter Bühlmann, Hansruedi Künsch, 1994 .

Supervised gene clustering with penalized logistic regression

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

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

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

Prediction and variable selection with the adaptive Lasso

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

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

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

Synchronizing multivariate financial time series

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

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

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

Finding predictive gene groups from microarray data

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

An algorithm for nonparametric GARCH modelling

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

Goodness of fit tests for high-dimensional models

Peter Bühlmann, Rajen D. Shah, 2015 .

Efficient and adaptive post-model-selection estimators

Peter Bühlmann, P. Bühlmann, 1999 .

The blockwise bootstrap for general parameters of a strationary time series

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

Tree‐structured generalized autoregressive conditional heteroscedastic models

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

Robustified L 2 Boosting

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

Variable length Markov chains: methodology, computing and software

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

Discussions of boosting papers, and rejoinders

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

The group Lasso

Sara van de Geer, Peter Bühlmann, 2011 .

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

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

Hierarchical Testing in the High-Dimensional Setting With Correlated Variables

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

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 .

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.

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

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

Regression shrinkage and selection via the Lasso: a retrospective (Robert Tibshirani): Comments on the presentation

Peter Bühlmann, 2011 .

Volatility and risk estimation with linear and nonlinear methods based on high frequency data

Peter Bühlmann, Marcel Dettling, 2001 .

Simultaneous analysis of large-scale RNAi screens for pathogen entry

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

Prediction of Spatial Cumulative Distribution Functions Using Subsampling: Comment

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

Network analysis of systems elements.

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

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

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

High dimensional sparse covariance estimation via directed acyclic graphs

Peter Bühlmann, Philipp Rütimann, 2009 .

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.

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

Peter Bühlmann, P. Bühlmann, 2002 .

Moving-average representation of autoregressive approximations

Peter Bühlmann, P. Bühlmann, 1995 .

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.

Structure Learning with Bow-free Acyclic Path Diagrams

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

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

Peter Bühlmann, P. Bühlmann, 2000 .

Conjugate Direction Boosting

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

Robustification of the PC-Algorithm for Directed Acyclic Graphs

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