Peter Bühlmann
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Swiss mathematical statistician
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Peter Bühlmannmathematics Degrees
Mathematics
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Statistics
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Mathematics
Peter Bühlmann's Degrees
- PhD Mathematics ETH Zurich
Why Is Peter Bühlmann Influential?
(Suggest an Edit or Addition)According to Wikipedia, Peter Lukas Bühlmann is a Swiss mathematician and statistician. Biography Bühlmann studied mathematics from 1985 at the ETH Zurich with Diplom in 1990 and doctorate in 1993. His thesis The Blockwise Bootstrap in Time Series and Empirical Processes was written under the supervision of Hans-Rudolf Künsch and Erwin Bolthausen. At the University of California, Berkeley, Bühlmann was from 1994 to 1995 a postdoctoral research fellow and from 1995 to 1997 Neyman Assistant Professor. At ETH Zurich he became assistant professor in 1997 and is a full professor from 2004 to the present. From 2013 to 2017 he chaired the Department of Mathematics.
Peter Bühlmann's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- MissForest - non-parametric missing value imputation for mixed-type data (2011) (2610)
- The group lasso for logistic regression (2008) (1648)
- Statistics for High-Dimensional Data (2011) (1255)
- A systematic comparison and evaluation of biclustering methods for gene expression data (2006) (970)
- Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm (2005) (788)
- Analyzing gene expression data in terms of gene sets: methodological issues (2007) (750)
- Analyzing Bagging (2001) (622)
- Boosting With the L2 Loss (2003) (562)
- Causal Inference Using Graphical Models with the R Package pcalg (2012) (545)
- Boosting with the L2-loss: regression and classification (2001) (491)
- Survival ensembles. (2006) (473)
- Sieve bootstrap for time series (1997) (429)
- p-Values for High-Dimensional Regression (2008) (403)
- Variable Length Markov Chains (1999) (401)
- Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana (2004) (398)
- Boosting for Tumor Classification with Gene Expression Data (2003) (327)
- Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs (2011) (297)
- Sure independence screening for ultrahigh dimensional feature space Discussion (2008) (297)
- ℓ1-penalization for mixture regression models (2010) (291)
- Bootstraps for Time Series (2002) (276)
- Predicting causal effects in large-scale systems from observational data (2010) (252)
- High-Dimensional Statistics with a View Toward Applications in Biology (2014) (247)
- Bagging, Boosting and Ensemble Methods (2012) (219)
- Systems-based analysis of Arabidopsis leaf growth reveals adaptation to water deficit (2012) (205)
- Targeted Quantitative Analysis of Streptococcus pyogenes Virulence Factors by Multiple Reaction Monitoring*S (2008) (199)
- CAM: Causal Additive Models, high-dimensional order search and penalized regression (2013) (198)
- Model-based Boosting 2.0 (2010) (193)
- Gene Expression Signatures Identify Rhabdomyosarcoma Subtypes and Detect a Novel t(2;2)(q35;p23) Translocation Fusing PAX3 to NCOA1 (2004) (192)
- Supervised clustering of genes (2002) (176)
- Block length selection in the bootstrap for time series (1999) (169)
- Estimation for High‐Dimensional Linear Mixed‐Effects Models Using ℓ1‐Penalization (2010) (163)
- Finding predictive gene groups from microarray data (2004) (140)
- Low-Order Conditional Independence Graphs for Inferring Genetic Networks (2006) (136)
- Boosting for high-dimensional linear models (2006) (134)
- High-dimensional learning of linear causal networks via inverse covariance estimation (2013) (124)
- High-dimensional simultaneous inference with the bootstrap (2016) (121)
- Methods for causal inference from gene perturbation experiments and validation (2016) (115)
- Sparse Boosting (2006) (109)
- Blockwise Bootstrapped Empirical Process for Stationary Sequences (1994) (104)
- An algorithm for nonparametric GARCH modelling (2002) (100)
- Sieve bootstrap for smoothing in nonstationary time series (1998) (99)
- Two optimal strategies for active learning of causal models from interventional data (2012) (94)
- Conditional transformation models (2012) (91)
- GLMMLasso: An Algorithm for High-Dimensional Generalized Linear Mixed Models Using ℓ1-Penalization (2011) (90)
- Arabidopsis GERANYLGERANYL DIPHOSPHATE SYNTHASE 11 is a hub isozyme required for the production of most photosynthesis-related isoprenoids. (2016) (90)
- Mining Tissue Microarray Data to Uncover Combinations of Biomarker Expression Patterns that Improve Intermediate Staging and Grading of Clear Cell Renal Cell Cancer (2009) (88)
- Most Likely Transformations (2015) (86)
- Discovery of TNF inhibitors from a DNA-encoded chemical library based on diels-alder cycloaddition. (2009) (86)
- High-dimensional Covariance Estimation Based On Gaussian Graphical Models (2010) (85)
- Missing values: sparse inverse covariance estimation and an extension to sparse regression (2012) (84)
- Bagging, subagging and bragging for improving some prediction algorithms (2003) (81)
- 0-PENALIZED MAXIMUM LIKELIHOOD FOR SPARSE DIRECTED ACYCLIC GRAPHS BY SARA (2013) (80)
- Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs (2013) (78)
- Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables (2011) (78)
- Causal stability ranking (2011) (73)
- A new mixing notion and functional central limit theorems for a sieve bootstrap in time series (1999) (73)
- Consistent neighbourhood selection for sparse high-dimensional graphs with the Lasso (2004) (72)
- High-dimensional variable screening and bias in subsequent inference, with an empirical comparison (2014) (71)
- Structural Intervention Distance for Evaluating Causal Graphs (2015) (71)
- Statistical significance in high-dimensional linear models (2013) (70)
- Variable Length Markov Chains: Methodology, Computing, and Software (2004) (69)
- Invariant Causal Prediction for Sequential Data (2017) (68)
- Twin Boosting: improved feature selection and prediction (2010) (62)
- Model-based boosting in high dimensions (2006) (60)
- Robustification of the PC-Algorithm for Directed Acyclic Graphs (2008) (59)
- LOCALLY ADAPTIVE LAG‐WINDOW SPECTRAL ESTIMATION (1996) (57)
- Moving-average representation of autoregressive approximations (1995) (54)
- Weak dependence beyond mixing and asymptotics for nonparametric regression (2002) (53)
- The blockwise bootstrap for general parameters of a strationary time series (1993) (50)
- Understanding human functioning using graphical models (2010) (49)
- Volatility estimation with functional gradient descent for very high-dimensional financial time series (2003) (49)
- Model Selection for Variable Length Markov Chains and Tuning the Context Algorithm (2000) (49)
- Lower bounds for the number of false null hypotheses for multiple testing of associations under general dependence structures (2005) (47)
- Protein and gene model inference based on statistical modeling in k-partite graphs (2010) (45)
- Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries (2007) (45)
- A multi-marker association method for genome-wide association studies without the need for population structure correction (2016) (45)
- Tree structured GARCH models (2000) (45)
- Simultaneous analysis of large-scale RNAi screens for pathogen entry (2014) (42)
- Tree‐structured generalized autoregressive conditional heteroscedastic models (2001) (42)
- Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data (2014) (41)
- Assessing statistical significance in multivariable genome wide association analysis (2016) (39)
- Hierarchical Testing in the High-Dimensional Setting With Correlated Variables (2013) (39)
- Causal Structure Learning and Inference: A Selective Review (2014) (38)
- The blockwise bootstrap for general empirical processes of stationary sequences (1995) (37)
- The blockwise bootstrap in time series and empirical processes (1993) (37)
- Splines for financial volatility (2007) (35)
- What is a linear process? (1996) (33)
- Robustified L2 boosting (2008) (33)
- Closure of Linear Processes (1997) (29)
- Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome (2012) (29)
- Statistical Approach to Protein Quantification* (2013) (28)
- Causal statistical inference in high dimensions (2013) (28)
- Score-based causal learning in additive noise models (2013) (27)
- Statistics for big data: A perspective (2018) (27)
- Two Optimal Strategies for Active Learning of Causal Models from Interventions (2012) (26)
- Pattern alternating maximization algorithm for missing data in high-dimensional problems (2014) (24)
- Decomposition and Model Selection for Large Contingency Tables (2009) (23)
- How to use boosting for tumor classification with gene expression data (2002) (23)
- Consistency for L₂boosting and matching pursuit with trees and tree-type basis functions (2002) (22)
- Synchronizing multivariate financial time series (2004) (21)
- Extreme events from the return-volume process: a discretization approach for complexity reduction (1998) (21)
- Rejoinder: ℓ1-penalization for mixture regression models (2010) (20)
- Gene expression profiles and risk stratification in childhood acute lymphoblastic leukemia. (2004) (20)
- Sieve Bootstrap With Variable-Length Markov Chains for Stationary Categorical Time Series (2002) (19)
- Discussion of “The Evolution of Boosting Algorithms” and “Extending Statistical Boosting” (2014) (19)
- Change-Point Detection for Graphical Models in the Presence of Missing Values (2019) (19)
- DOUBLY DEBIASED LASSO: HIGH-DIMENSIONAL INFERENCE UNDER HIDDEN CONFOUNDING. (2020) (18)
- Hypersurfaces and Their Singularities in Partial Correlation Testing (2012) (17)
- Boosting, model selection, lasso and nonnegative garrote (2005) (16)
- Hierarchical inference for genome-wide association studies: a view on methodology with software (2018) (16)
- Domain adaptation under structural causal models (2020) (16)
- Annotating novel genes by integrating synthetic lethals and genomic information (2008) (15)
- Multiomic profiling of the liver across diets and age in a diverse mouse population. (2020) (15)
- Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression (2020) (14)
- A Sequential Rejection Testing Method for High-Dimensional Regression with Correlated Variables (2015) (14)
- Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise (2018) (13)
- Efficient and adaptive post-model-selection estimators (1999) (12)
- Conjugate Direction Boosting (2006) (12)
- Controlling false positive selections in high-dimensional regression and causal inference (2013) (12)
- Structure Learning with Bow-free Acyclic Path Diagrams (2015) (11)
- Invariance, Causality and Robustness. (2018) (11)
- Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries (2020) (11)
- High dimensional sparse covariance estimation via directed acyclic graphs (2009) (10)
- The group Lasso (2011) (10)
- High-dimensional statistics, with applications to genome-wide association studies (2017) (9)
- Prediction and variable selection with the adaptive Lasso (2010) (9)
- Volatility and risk estimation with linear and nonlinear methods based on high frequency data (2004) (9)
- Lasso for linear models (2011) (8)
- EVE (external variance estimation) increases statistical power for detecting differentially expressed genes. (2007) (7)
- Robustified L 2 Boosting (2007) (7)
- Network analysis of systems elements. (2007) (7)
- Discussions of boosting papers, and rejoinders (2004) (7)
- Dynamic adaptive partitioning for nonlinear time series (1999) (7)
- Theory for the Lasso (2011) (7)
- Goodness-offit testing in high-dimensional generalized linear models (2019) (6)
- Prediction of Spatial Cumulative Distribution Functions Using Subsampling: Comment (1999) (6)
- Comparison of Biclustering Methods : A Systematic Comparison and Evaluation of Biclustering Methods for Gene Expression Data (2006) (6)
- Nonparametric GARCH models (1999) (6)
- Distributional anchor regression (2021) (6)
- Boosting With the L 2 Loss : Regression and Classi cation (2008) (6)
- A Fast Non-parametric Approach for Causal Structure Learning in Polytrees (2021) (6)
- Statistical Approach to Protein Quantification. (2014) (6)
- Statistical Analysis of Quantum Chemical Data Using Generalized XML/CML Archives for the Derivation of Molecular Design Rules (2007) (5)
- Deconfounding and Causal Regularisation for Stability and External Validity (2020) (5)
- On the Identifiability and Estimation of Causal Location-Scale Noise Models (2022) (5)
- Selection of Credibility Regression Models (1999) (5)
- Boosting (2011) (5)
- Structure Learning for Directed Trees (2021) (5)
- Boosting Algorithms: Regularization, Prediction and Model Fitting. Rejoinder. (2007) (5)
- High-dimensional variable screening and bias in subsequent inference, with an empirical comparison (2013) (4)
- Goodness of fit tests for high-dimensional models (2015) (4)
- The Molecular Landscape of the Aging Mouse Liver (2020) (4)
- Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020) (2020) (4)
- Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables (2022) (4)
- Remembrance of Leo Breiman (2010) (4)
- Variable length Markov chains: methodology, computing and software (2002) (4)
- Ensemble Methods of Computational Inference (2005) (3)
- Non-convex loss functions and ℓ 1 -regularization (2011) (3)
- BOOSTING: A STATISTICAL PERSPECTIVE (2006) (3)
- Toward causality and improving external validity (2020) (3)
- Rejoinder: Invariance, Causality and Robustness (2020) (3)
- Sparse Contingency Tables and High-Dimensional Log-Linear Models for Alternative Splicing in Full-Length cDNA Libraries (2006) (3)
- Supervised gene clustering with penalized logistic regression (2003) (3)
- SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research (2020) (2)
- Comments on: Data science, big data and statistics (2019) (2)
- A Look at Robustness and Stability of 1-versus 0-Regularization : Discussion of Papers by Bertsimas et al . and Hastie et al . (2020) (2)
- Sieve bootstrap with variable length Markov chains for stationary categorical time series (1999) (2)
- Variable selection with the Lasso (2011) (2)
- Mathematics, Statistics and Data Science (2016) (2)
- Discussion on: Sparse regression: Scalable algorithms and empirical performance & Best Subset, Forward Stepwise, or Lasso? Analysis and recommendations based on extensive comparisons (2020) (2)
- Boosting and l1-Penalty Methods for High-dimensional Data with Some Applications in Genomics (2005) (2)
- Partial Least Squares for Heterogeneous Data (2014) (2)
- Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics (2022) (2)
- Upper bounds for the number of true null hypotheses and novel estimates for error rates in multiple testing (2004) (2)
- Comment to “ ` 1-Penalization for Mixture Regression Models ” by (2010) (2)
- Boosting Algorithms: with an Application to Bootstrapping Multivariate Time Series (2006) (2)
- Generalized linear models and the Lasso (2011) (2)
- A Look at Robustness and Stability of $\ell_{1}$-versus $\ell_{0}$-Regularization: Discussion of Papers by Bertsimas et al. and Hastie et al. (2020) (2)
- Missing values and sparse inverse covariance estimation (2009) (1)
- Higher-order least squares: assessing partial goodness of fit of linear regression (2021) (1)
- Simultaneous analysis of large-scale RNAi screens for pathogen entry (2014) (1)
- Some Themes in High-Dimensional Statistics (2016) (1)
- Causal statistical inference in high dimensions (2013) (1)
- Missing values: sparse inverse covariance estimation and an extension to sparse regression (2010) (1)
- Statistical approach to absolute protein quantification (2011) (1)
- Comments on: A random forest guided tour (2016) (1)
- Theory for ℓ 1 /ℓ 2 -penalty procedures (2011) (1)
- Causal inference in high dimensions II (2012) (1)
- Single-cell mapping of tumor heterogeneity in pediatric rhabdomyosarcoma reveals developmental signatures with therapeutic relevance (2022) (1)
- Higher-order least squares: assessing partial goodness of fit of linear causal models (2021) (1)
- ST ] 4 N ov 2 01 6 Kernel-based Tests for Joint Independence (2018) (1)
- Boosting and greedy algorithms (2011) (1)
- Discussion on ‘regularized regression for categorical data (Tutz and Gertheiss)’ (2016) (1)
- Modeling Inhomogeneous High-Dimensional Data-Sets: with Applications inLearning Large-Scale Gene Correlations (2007) (1)
- Single-cell profiling of alveolar rhabdomyosarcoma reveals RAS pathway inhibitors as cell-fate hijackers with therapeutic relevance (2023) (1)
- mboost Illustrations (2007) (1)
- Invariance, Causality and Robustness 2018 Neyman Lecture (2019) (1)
- Identifying cancer pathway dysregulations using differential causal effects (2021) (1)
- P-values for linear models and beyond (2011) (0)
- Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements (2021) (0)
- Regularized Double Machine Learning in Partially Linear Models with Unobserved Confounding (2021) (0)
- Statistics for High-Dimensional Data: Selected Topics, part 1 (2014) (0)
- PENALIZED LIKELIHOOD AND BAYESIAN METHODS FOR SPARSE CONTINGENCY TABLES: AN ANALYSIS OF ALTERNATIVE SPLICING IN FULL-LENGTH cDNA LIBRARIES (2006) (0)
- Rejoinder on: High-dimensional simultaneous inference with the bootstrap (2017) (0)
- Hierarchical inference for genome-wide association studies: a view on methodology with software (2020) (0)
- One Modern Culture of Statistics: Comments on Statistical Modeling: The Two Cultures (Breiman, 2001b) (2021) (0)
- Statistical Inference for Big Data (2014) (0)
- On the role of additive regression for (high-dimensional) causal inference (2014) (0)
- Bootstrap schemes for time series (in Russian) (2007) (0)
- Plug‐in Machine Learning for Partially Linear Mixed‐Effects Models with Repeated Measurements (2021) (0)
- Confidence Intervals and Tests for High-Dimensional Models: A Compact Review (2015) (0)
- M L ] 3 D ec 2 00 8 High-Dimensional Additive Modeling (2009) (0)
- Marginal integration for fully robust causal inference (2014) (0)
- INVARIANCE IN HETEROGENEOUS, LARGE-SCALE AND HIGH-DIMENSIONAL DATA (2019) (0)
- Frontiers in Nonparametric Statistics (2012) (0)
- Chapter 16 : Part II-Network Analysis of Systems Elements (2006) (0)
- Comments on: Data science, big data and statistics (2019) (0)
- Histograms showing the empirical distribution of scores (left) and margins (right) for the leukemia dataset (AML/ALL distinction), based on 1,000 bootstrap replicates with permuted response variables (2011) (0)
- Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software (2020) (0)
- M L ] 2 5 Ju n 20 08 High-Dimensional Additive Modeling (2009) (0)
- Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software (2020) (0)
- Statistical Recovery of Discrete, Geometric and Invariant Structures (2018) (0)
- groupICA: Independent component analysis for grouped data (2018) (0)
- M L ] 2 F eb 2 00 9 High-Dimensional Additive Modeling (2009) (0)
- Assessing the goodness of fit of linear regression via higher-order least squares (2021) (0)
- Hierarchical inference for genome-wide association studies (2020) (0)
- High-dimensional simultaneous inference with the bootstrap (2017) (0)
- DFG-SNF Research Group FOR 916 Statistical Regularization and Qualitative Constraints (2010) (0)
- Hypersurfaces and Their Singularities in Partial Correlation Testing (2014) (0)
- Computational Statistics Spring 2008 (2011) (0)
- 2 Predicting potential outcomes , heterogeneity and worst case risk optimization (2018) (0)
- Comment (2011) (0)
- 2 Logistic Group Lasso 2 . 1 Model Setup (2007) (0)
- Discoveries at risk (2003) (0)
- Using synthetic lethality and integration of genomic data for finding spindle migration genes (2006) (0)
- Additive models and many smooth univariate functions (2011) (0)
- Dynamic combination of models for nonlinear time series (2002) (0)
- Probability and moment inequalities (2011) (0)
- Assigning statistical significance in high-dimensional problems (2012) (0)
- repliclust: Synthetic Data for Cluster Analysis (2023) (0)
- Discussion of Big Bayes Stories and BayesBag (2014) (0)
- Perturbations and Causality in Gaussian Models (2021) (0)
- Rejoinder: Invariance, Causality and Robustness (2018 Neyman Lecture) (2020) (0)
- Rejoinder on: High-dimensional simultaneous inference with the bootstrap (2017) (0)
- OPTIMALITY OF THE WESTFALL – YOUNG PERMUTATION PROCEDURE 3 see also Blanchard and Roquain [ 4 ] for FDR control under dependence (2012) (0)
- The Mathematics of High-Dimensional Statistics (2014) (0)
- Comments on: A random forest guided tour (2016) (0)
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