Bühlmann, P. and van de Geer,
S. (2011).
Statistics for High-Dimensional Data: Methods, Theory and
Applications. Springer.
Statistical
Analysis for High-Dimensional Data. The Abel Symposium 2014. Edited
by Frigessi, A., Bühlmann, P., Glad, I.K., Langaas, M., Richardson,
S. and Vannucci, M. (2016). Springer.
Handbook
of Big Data. Edited by Bühlmann, P., Drineas, P., Kane, M. and
van der Laan, M. (2016). Chapman & Hall/CRC.
Preprints
Londschien, M. and Bühlmann, P. (2024). Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets. Preprint arXiv:2407.15256
Gu, Y., Fang, C., Bühlmann, P. and Fan, J. (2024). Causality pursuit from heterogeneous environments via neural adversarial invariance learning. Preprint arXiv:2405.04715
Schultheiss, C. and Bühlmann, P. (2024). Ancestor regression in structural vector autoregressive models. Preprint arXiv:2403.03778
Pfister, N. and Bühlmann, P. (2024). Extrapolation-aware nonparametric statistical inference. Preprint arXiv:2402.09758
Law, M., Bühlmann, P. and Ritov, Y. (2023). Distributional
robustness and transfer learning through Empirical Bayes. Preprint arXiv:2312.08485
Scheidegger, C., Guo, Z. and Bühlmann, P. (2023). Spectral
deconfounding for high-dimensional sparse additive models. Preprint arXiv:2312.02860
Wang, Z., Bühlmann, P. and Guo, Z (2023). Distributionally robust
machine learning with multi-source data. Preprint arXiv:2309.02211
Shen, X., Bühlmann, P. and Taeb, A. (2023). Causality-oriented
robustness: exploiting general additive interventions. Preprint arXiv:2307.10299
Zellinger, M.J. and Bühlmann, P. (2023). repliclust: synthetic data
for cluster analysis. Preprint arXiv:2303.14301
Gamella, J.L., Taeb, A., Heinze-Deml, C. and Bühlmann, P. (2022). Characterization and greedy learning of Gaussian structural causal models under unknown interventions. Preprint arXiv:2211.14897
Emmenegger, C., Spohn, M.-L., Elmer, T. and Bühlmann, P. (2022). Treatment
effect estimation from observational network data using augmented inverse
probability weighting and machine learning. Preprint arXiv:2206.14591
Guo, Z., Zheng, M. and Bühlmann, P. (2022). Robustness against
weak or invalid instruments: exploring nonlinear treatment models with
machine
learning. Preprint arXiv:2203.12808
2024
Gamella, J.L., Peters, J. and Bühlmann, P. (2024). The Causal
Chambers as a real-world physical testbed for AI methodology. To appear in
Nature Machine Intelligence. Preprint arXiv:2404.11341
Qian, L., Sun, R., Aebersold, R., Bühlmann, P., Sander,
C. and Guo, T. (2024). AI-empowered perturbation proteomics for complex biological systems. To appear in Cell Genomics.
Henzi, A., Shen, X., Law, M. and Bühlmann, P. (2023). Invariant
probabilistic prediction. To appear in Biometrika. Preprint arXiv:2309.10083
Taeb, A., Gamella, J.L., Heinze-Deml, C. and Bühlmann,
P. (2021). Learning and scoring Gaussian latent variable causal models
with unknown additive interventions. To appear in the Journal of Machine Learning Research. Preprint arXiv:2101.06950
Kovács, S., Li, H., Haubner, L., Munk, A. and Bühlmann,
P. (2020). Optimistic search strategy: Change point detection for
large-scale data via adaptive logarithmic queries. To appear in the
Journal of Machine Learning Research. Preprint arXiv:2010.10194
Carl, D., Emmenegger, C., Bühlmann, P. and Guo, Z. (2023). TSCI:
two stage curvature identification for causal inference with invalid
instruments. To appear in the Journal of Statistical Software. Preprint arXiv:2304.00513
Schultheiss, C., Bühlmann, P. and Yuan, M. (2024). Assessing the
goodness of fit of linear regression via higher-order least
squares. Journal of the American Statistical Association 119, 1019-1031. Download
Schultheiss, C. and Bühlmann, P. (2024). Assessing the overall
and partial causal well-specification of nonlinear additive noise
models. Journal of Machine Learning Research 25, (159): 1-41. Download
Taeb, A., Bühlmann, P. and Chandrasekaran, V. (2024). Model
selection over partially ordered sets. Proceedings of the National
Academy of Sciences USA 121, No. 8, e314228121: 1-12. Download
2023
Näf, J., Emmenegger, C., Bühlmann, P. and Meinshausen,
N. (2023). Confidence and uncertainty assessment for distributional
Random Forests. Journal of Machine Learning Research 24, (366): 1-77. Download
Immer, A., Schultheiss, C., Vogt, J.E., Schölkopf, B.,
Bühlmann, P. and Marx, A. (2023). On the identifiability and
estimation of causal location-scale noise models. Proceedings of the
40th International Conference on Machine Learning (ICML), PMLR
202:14316-14332, 2023. Download
Schultheiss, C. and Bühlmann, P. (2023). Ancestor regression in linear structural equation models. Biometrika 110, 1117-1124. Download
Emmenegger, C. and Bühlmann, P. (2023). Plug-in machine
learning for partially linear mixed-effects models with repeated
measurements. Scandinavian Journal of Statistics 50, 1553-1567. Download
Law, M. and Bühlmann, P. (2023). Discussion of “A Scale-Free
Approach for False Discovery Rate Control in Generalized Linear Models
(Dai, Lin, Xing and Liu)”. Journal of the American
Statistical Association 118, 1578-1583. Download
Rothenhäusler, D. and Bühlmann, P. (2023). Distributionally
robust and generalizable inference. Statistical
Science, 38, No. 4, 527-542. Download
Londschien, M., Bühlmann, P. and Kovács,
S. (2023). Random Forests for change point detection. Journal of Machine
Learning Research 24, (216): 1-45. Download
Moor, M., Bennett, N., Plecko, D., Horn, M., Rieck, B., Meinshausen,
N., Bühlmann, P. and Borgwardt, K. (2023). Predicting sepsis using
deep learning across international sites: a retrospective development and
validation study. eClinicalMedicine 62, Article 103124, 1-13. Download
Bennett, N., Plecko, D., Ukor, I.-F., Meinshausen, N. and
Bühlmann, P. (2023). ricu: R's interface to intensive care
data. GigaScience 12, giad041, 1-8. Download
Schultheiss, C. and Bühlmann, P. (2023). On the pitfalls of
Gaussian likelihood scoring for causal discovery. Journal of Causal
Inference 11(1), jci-2022-0068, 1-11. Download
Shah, R.D. and Bühlmann, P. (2023). Double-estimation-friendly
inference for high-dimensional misspecified models. Statistical Science
38, 68-91. Download
Kovács, S., Bühlmann, P., Li, H. and Munk, A. (2023). Seeded binary segmentation: a general methodology for fast and optimal change point detection. Biometrika 110, 249-256. Download
Danielli, S.G., Porpiglia, E., De Micheli, A.J., Navarro, N.,
Zellinger, M.J., Bechtold, I., Kisele, S., Volken, L., Marques, J.G.,
Kasper, S., Bode, P.K., Henssen, A.G., Gürgen, D., Delattre, O.,
Surdez, D., Roma, J., Bühlmann, P., Blau, H.M., Wachtel, M. and
Schäfer, B.W. (2023). Single-cell profiling of alveolar
rhabdomyosarcoma reveals RAS pathway inhibitors as cell-fate
hijackers with therapeutic relevance. Science Advances 9,
eade9238, 1-20. Download
Marmolejo-Ramos, F., Tejo, M., Brabec, M., Kuzilek, J., Joksimovic,
S., Kovanovic, V., González, J., Kneib, T., Bühlmann, P., Kook,
L., Briseño-Sánchez, G., Ospina,
R. (2023). Distributional regression modeling via generalized additive
models for location, scale, and shape: An overview through a dataset
from learning analytics. WIREs Data Mining and Knowledge Discovery 13,
e1479, 1-22. Download
2022
Ćevid, D., Michel, L., Näf, J., Bühlmann,
P. and Meinshausen, N. (2022). Distributional Random Forests:
heterogeneity adjustment and multivariate distributional
regression. Journal of Machine Learning Research 23, (333): 1-79. Download
Scheidegger, C., Hörrmann, J. and Bühlmann, P. (2022). The
weighted generalised covariance measure. Journal of
Machine Learning Research 23, (273): 1-68. Download
Guo, Z., Ćevid, D. and Bühlmann, P. (2022). Doubly
Debiased Lasso: high-dimensional inference under hidden
confounding. Annals of Statistics 50, 1320-1347. Download
Jakobsen, M.E., Shah, R.D., Bühlmann, P. and Peters,
J. (2022). Structure learning for directed trees. Journal of Machine
Learning Research 23, (159): 1-97. Download
Kook, L., Sick, B. and Bühlmann, P. (2022). Distributional anchor
regression. Statistics and Computing 32: 39, 1-19. Download
Jablonski, K.P., Pirkl, M., Ćevid, D., Bühlmann, P. and
Beerenwinkel, N. (2022). Identifying cancer pathway dysregulations using
differential causal effects. Bioinformatics 38, 1550-1559. Download
Williams, E.G., Pfister, N., Roy, S., Statzer, C., Ingels, J., Bohl,
C., Hasan, M., Cuklina, J., Bühlmann, P., Zamboni, N., Lu, L.,
Ewald, C.Y., Williams, R.W. and Aebersold, R. (2022). Multiomic profiling of the liver across diets and age in a diverse mouse population. Cell Systems 13,
43-57. Download Preprint bioRxiv:2020.08.20.222968
2021
Guo, Z., Renaux, C., Bühlmann, P. and Cai, T.T. (2021). Group
inference in high dimensions with applications to hierarchical
testing. Electronic Journal of Statistics 15, 6633-6676. Download
Emmenegger, C. and Bühlmann, P. (2021). Regularizing double
machine learning in partially linear endogenous models. Electronic
Journal of Statistics 15, 6461-6543. Download
Chen, Y. and Bühlmann, P. (2021). Domain adaptation under
structural causal models. Journal of Machine Learning Research 22, (261):
1-80. Download
Pfister, N., Williams, E.G., Peters, J., Aebersold, R. and Bühlmann,
P. (2021). Stabilizing variable selection and regression. Annals of
Applied Statistics 15, 1220-1246. Download
Londschien, M., Kovács, S. and Bühlmann, P. (2021). Change
point detection for graphical models in presence of missing
values. Journal of Computational and Graphical Statistics 30, 768-779. Download
Bühlmann, P. (2021). One modern culture of statistics. Comments
on Statistical Modeling: The Two Cultures (Breiman, 2001b). Observational
Studies 7, 33-40. Download
Rothenhäusler, D., Meinshausen, N., Bühlmann, P. and Peters,
J. (2021). Anchor regression: heterogeneous data meet
causality. Journal of the Royal Statistical Society,
Series B 83, 215-246. Download
Schultheiss, C., Renaux, C. and Bühlmann, P. (2021). Multicarving
for high-dimensional post-selection inference. Electronic Journal of
Statistics 15, 1695-1742. Download
2020
Bühlmann, P. and Ćevid, D. (2020). Deconfounding and
causal regularization for stability and external validity. International
Statistical Review 88, S114-S134. Download
Ćevid, D., Bühlmann, P. and Meinshausen,
N. (2020). Spectral deconfounding via perturbed sparse linear
models. Journal of Machine Learning Research 21, (232): 1-41. Download
Kovács, S., Li, H. and Bühlmann, P. (2020). Seeded
intervals and noise level estimation in change point detection: A
discussion of Fryzlewicz (2020). Journal of the Korean
Statistical Society 49, 1081-1089. Download
Chen, Y., Taeb, A. and Bühlmann, P. (2020). A look at robustness
and stability of l1- versus l0-regularization: Discussion of
papers by Bertsimas et al. and Hastie et al. Statistical Science 35,
614-622. Download
Bühlmann, P. (2020). Toward causality and improving external
validity. Proceedings of the National Academy of Sciences
USA 117,
25963-25965. Download
Janková, J., Shah, R.D., Bühlmann, P. and Samworth
R.J. (2020). Goodness-of-fit testing in high-dimensional generalized
linear models. Journal of the Royal Statistical Society, Series
B 82, 773-795. Download
Bühlmann, P. (2020). Rejoinder: Invariance, Causality and
Robustness. Statistical Science 35, 434-436. Download
Bühlmann, P. (2020). Invariance, Causality and
Robustness (with discussion). Statistical Science 35, 404-426. Download
Renaux, C., Buzdugan, L., Kalisch, M. and Bühlmann,
P. (2020). Rejoinder on: Hierarchical inference for genome-wide
association studies: a view on methodology with software. Computational
Statistics 35, 59-67. Download
Renaux, C., Buzdugan, L., Kalisch, M. and Bühlmann,
P. (2020). Hierarchical inference for genome-wide association studies: a
view on methodology with software (with discussion). Computational
Statistics 35, 1-40. Download
2019
Pfister, N., Weichwald, S., Bühlmann, P. and Schölkopf,
B. (2019). Robustifying Independent Component Analysis by adjusting for
group-wise stationary noise. Journal of Machine Learning Research 20,
(147):1-50. Download
Bühlmann, P. (2019). Comments on: Data science, big data and
statistics. TEST 28, 330-333. Download
Rothenhäusler, D., Bühlmann, P. and Meinshausen,
N. (2019). Causal Dantzig: fast inference in linear structural equation
models with hidden variables under additive interventions. Annals of
Statistics 47, 1688-1722. Download
Pfister, N., Bühlmann, P. and Peters, J. (2019). Invariant causal
prediction for sequential data. Journal of the American
Statistical Association 114, 1264-1276.Download
2018
Rothenhäusler, D., Ernest, J. and Bühlmann,
P. (2018). Causal inference in partially linear structural equation
models. Annals of Statistics 46, 2904-2938. Download
Bühlmann, P. and van de Geer, S. (2018). Statistics for big data:
A perspective. Statistics & Probability Letters 136, 37-41. Download
Hothorn, T., Möst, L. and Bühlmann, P. (2018). Most likely
transformations. Scandinavian Journal of Statistics 45, 110-134. Download
Shah, R.D. and Bühlmann, P. (2018). Goodness of fit tests for
high-dimensional linear models. Journal of the Royal Statistical Society,
Series B 80, 113-135. Download
Pfister, N., Bühlmann, P., Schölkopf, B. and Peters,
J. (2018). Kernel-based tests for joint independence. Journal of the
Royal Statistical Society, Series B 80, 5-31. Download
2017
Nowzohour, C., Maathuis, M.H., Evans, R.J. and Bühlmann,
P. (2017). Distributional equivalence and structure learning for bow-free
acyclic path diagrams. Electronic Journal of Statistics 11, 5342-5374. Download
Dezeure, R., Bühlmann, P. and Zhang,
C.-H. (2017). High-dimensional simultaneous inference with the
bootstrap (with discussion). TEST 26,
685-719. Download
Dezeure, R., Bühlmann, P. and Zhang,
C.-H. (2017). Rejoinder on: High-dimensional simultaneous inference with the
bootstrap. TEST 26, 751-758. Download
Bühlmann, P. (2017). High-dimensional statistics, with applications
to genome-wide association studies. EMS Surveys in Mathematical
Sciences 4, 45-75. Preprint PDF
Li, S., Ernest, J. and Bühlmann, P. (2017). Nonparametric causal
inference from observational time series through marginal
integration. Econometrics and Statistics 2, 81-105. PDF
2016
Klasen, J.R., Barbez, E., Meier, L., Meinshausen, N., Bühlmann,
P., Koornneef, M., Busch, W. and Schneeberger, K. (2016). A multi-marker
association method for Genome-Wide Association studies without the need
for population structure correction. Nature Communications 7, Article
number 13299 (2016), doi:10.1038/ncomms13299. Download
Peters, J., Bühlmann, P. and Meinshausen, N. (2016). Causal
inference using invariant prediction: identification and confidence
intervals (with discussion). Journal of the Royal Statistical Society,
Series B 78, 947-1012. PDF
Buzdugan, L., Kalisch, M., Navarro, A., Schunk, D., Fehr, E. and
Bühlmann, P. (2016). Assessing statistical significance in
multivariable genome wide association analysis. Bioinformatics 32,
1990-2000. Download
Bühlmann, P. and Dezeure, R. (2016). Invited Discussion on
"Regularized regression for categorical data (Tutz and
Gertheiss)". Statistical Modelling: An International Journal 16, 205-211. PDF
Meinshausen, N., Hauser, A., Mooij, J.M., Peters, J., Versteeg, P. and
Bühlmann, P. (2016). Methods for causal inference from gene
perturbation experiments and validation. Proceedings of
the National Academy of Sciences USA 113,
7361-7368. PDF. Supporting Information
Mandozzi, J. and Bühlmann, P. (2016). A sequential rejection
testing method for high-dimensional regression with correlated
variables. International Journal of Biostatistics 12, 79-95. PDF
Nowzohour, C. and Bühlmann, P. (2016). Score-based causal
learning in additive noise models. Statistics 50, 471-485. PDF
Bühlmann, P. and Leonardi, F. (2016). Comments on: A random
forest guided tour. (Discussion on a paper by G. Biau and
E. Scornet). TEST 25, 239-246. PDF
Mandozzi, J. and Bühlmann, P. (2016). Hierarchical testing in the
high-dimensional setting with correlated
variables. Journal of the American Statistical Association 111,
331-343. PDF. Supplement
Rothenhäusler, D., Meinshausen, N. and Bühlmann,
P. (2016). Confidence intervals for maximin effects in inhomogeneous
large-scale data. In Statistical
Analysis for High-Dimensional Data. The Abel Symposium 2014 (eds. Frigessi, A., Bühlmann, P., Glad, I.K., Langaas, M., Richardson,
S. and Vannucci, M.), pp. 255-277. Springer. PDF
Bühlmann, P. and Meinshausen, N. (2016). Magging: maximin
aggregation for inhomogeneous large-scale data. Proceedings of the IEEE
104, 126-135. PDF
Ruiz-Sola, M.A., Coman, D., Beck, G., Barjaa, M.V., Colinas-Martinez,
M., Graf, A., Welsch, R., Rütimann, P., Bühlmann, P.,
Bigler, L., Gruissem, W., Rodriguez-Concepcion, M. and Vranova,
E. (2016). Arabidopsis geranylgeranyl diphosphate synthase 11 is a hub
isozyme required for the production of most photosynthesis-related
isoprenoids. New Phytologist 209, 252-264.
2015
Ernest, J. and Bühlmann, P. (2015). Marginal integration for
nonparametric causal inference. Electronic Journal of
Statistics 9, 3155-3194. Download
Dezeure, R., Bühlmann, P., Meier, L. and Meinshausen,
N. (2015). High-dimensional inference: confidence intervals, p-values and
R-software hdi. Statistical Science 30, 533-558. PDF
Bühlmann, P. and van de Geer, S. (2015). High-dimensional inference
in misspecified linear models. Electronic Journal of
Statistics 9,
1449-1473. Download
Meinshausen, N. and Bühlmann, P. (2015). Maximin effects in
inhomogeneous large-scale data. Annals of Statistics 43, 1801-1830. PDF
Peters, J. and Bühlmann, P. (2015). Structural intervention
distance (SID) for evaluating causal graphs. Neural Computation
27,771-799. PDF
Hauser, A. and Bühlmann, P. (2015). Jointly interventional and
observational data: estimation of interventional Markov equivalence
classes of directed acyclic graphs. Journal of the Royal
Statistical Society: Series B 77, 291-318. PDF
2014
Rämö, P., Drewek, A., Arrieumerlou, C.,
Beerenwinkel, N., Ben-Tekaya, H., Cardel, B., Casanova, A.,
Conde-Alvarez, R., Cossart, P., Csucs, G., Eicher, S., Emmenlauer, M.,
Greber, U., Hardt, W.-D., Helenius, A., Kasper, C., Kaufmann, A.,
Kreibich, S., Kühbacher, A., Kunszt, P., Low, S.H., Mercer, J.,
Mudrak, S., Muntwiler, S., Pelkmans, L., Pizarro-Cerda, J., Podvinec, M.,
Pujadas, E., Rinn, B., Rouilly, V., Schmich, F., Siebourg-Polster, J.,
Snijder, B., Stebler, M., Studer, G., Szczurek, E., Truttmann, M., von
Mering, C., Vonderheit, A., Yakimovich, A., Bühlmann, P. and Dehio,
C. (2014). Simultaneous analysis of large-scale RNAi screens for pathogen
entry. BMC Genomics 15:1162. Download
Bühlmann, P. (2014). Invited Discussion on "The Evolution of Boosting
Algorithms" and "Extending Statistical Boosting" (A. Mayr, H. Binder,
O. Gefeller and M. Schmid). Methods of Information in Medicine 53,
436-437.PDF
Bühlmann, P., Peters, J. and Ernest, J. (2014). CAM: Causal
Additive Models, high-dimensional order search and penalized
regression. Annals of Statistics 42, 2526-2556. PDF
Loh, P. and Bühlmann, P. (2014). High-dimensional learning of
linear causal networks via inverse covariance estimation. Journal of
Machine Learning Research 15, 3065-3105. PDF
Lin, S., Uhler, C., Sturmfels, B. and Bühlmann,
P. (2014). Hypersurfaces and their singularities in partial correlation
testing. Foundations of Computational Mathematics 14, 1079-1116. PDF
Städler, N., Stekhoven, D.J. and Bühlmann,
P. (2014). Pattern alternating maximization algorithm for missing data in
large p, small n problems. Journal of Machine Learning Research 15, 1903-1928.PDF
van de Geer, S., Bühlmann, P., Ritov, Y. and Dezeure, R. (2014). On
asymptotically optimal confidence regions and tests for high-dimensional
models. Annals of Statistics 42, 1166-1202. PDF
Bühlmann, P. and Mandozzi, J. (2014). High-dimensional variable
screening and bias in subsequent inference, with an empirical
comparison. Computational Statistics 29, 407-430.PDF
Bühlmann, P., Meier, L. and van de Geer, S. (2014). Invited Discussion on
"A significance test for the Lasso (R. Lockhart, J. Taylor,
R. Tibshirani and R. Tibshirani)". Annals of
Statistics 42, 469-477.PDF
Bühlmann, P. (2014). Invited Discussion of Big Bayes stories and
BayesBag. Statistical Science 29, 91-94.PDF
Schelldorfer, J., Meier, L. and Bühlmann, P. (2014). GLMMLasso:
An algorithm for high-dimensional generalized linear mixed models
using L1-penalization. Journal of Computational and Graphical
Statistics 23, 460-477.PDF
Hauser, A. and Bühlmann, P. (2014). Two optimal strategies for
active learning of causal models from interventional data. International
Journal of Approximate Reasoning 55, 926-939. (This is a longer
paper version of Hauser and Bühlmann (PGM 2012)).PDF
Peters, J. and Bühlmann, P. (2014). Identifiability of Gaussian
structural equation models with equal error variances. Biometrika 101,
219-228. PDF
Kalisch, M. and Bühlmann, P. (2014). Causal structure learning
and inference: a selective review. Quality Technology & Quantitative
Management 11, 3-21. Download
Gerster, S., Kwon, T., Ludwig, C., Matondo, M., Vogel, C.,
Marcotte, E., Aebersold, R. and Bühlmann, P. (2014). Statistical
approach to protein quantification. Molecular and Cellular
Proteomics 13, 666-677. Download
Bühlmann, P., Kalisch, M. and Meier, L. (2014). High-dimensional
statistics with a view towards applications in biology. Annual Review of
Statistics and its Applications 1, 255-278. Download
Hothorn, T., Kneib, T. and Bühlmann, P. (2014). Conditional
transformation models. Journal of the Royal Statistical
Society: Series B 76, 3-37. PDF
2013
Bühlmann, P., Rütimann, P. and Kalisch,
M. (2013). Controlling false positive selections in high-dimensional
regression and causal inference. Statistical Methods in
Medical Research 22, 466-492. PDF
Bühlmann, P., Rütimann, P., van de Geer, S. and Zhang,
C.-H. (2013). Correlated variables in regression: clustering and sparse
estimation (with discussion). Journal of Statistical Planning and
Inference 143, 1835-1871. PDF.
Rejoinder
Bühlmann, P. (2013). Statistical significance in high-dimensional
linear models. Bernoulli 19, 1212-1242. PDF
Bühlmann, P. (2013). Causal statistical inference in high
dimensions. Mathematical Methods of Operations Research 77, 357-370.PDF
van de Geer, S. and Bühlmann, P. (2013). l0-penalized maximum
likelihood for sparse directed acyclic graphs. Annals of
Statistics 41, 536-567. PDF
Uhler, C., Raskutti, G., Bühlmann, P. and Yu, B. (2013). Geometry
of faithfulness assumption in causal inference. Annals of
Statistics 41, 436-463. PDF
Fellinghauer, B., Bühlmann, P., Ryffel, M., von Rhein, M.,
Reinhardt, J.D. (2013). Stable graphical model estimation with Random
Forests for discrete, continuous, and mixed
variables. Computational Statistics & Data
Analysis 64, 132-152. PDF
2012
Stekhoven, D.J., Moraes, I., Sveinbjörnsson, G., Hennig, L.,
Maathuis, M.H. and Bühlmann, P. (2012). Causal stability
ranking. Bioinformatics 28, 2819-2823. PDF. Supplementary Data
Beleut, M., Zimmermann, P., Baudis, M., Bruni, N., Bühlmann, P.,
Laule, O., Luu, V.-D., Gruissem, W., Schraml, P. and Moch,
H. (2012). Integrative genome-wide expression profiling identifies three
distinct molecular subgroups of renal cell carcinoma with different patient
outcome. BMC Cancer 12:310. Download
Baerenfaller, K., Massonnet, C., Walsh, S., Baginsky, S., Bühlmann,
P., Hennig, L., Hirsch-Hoffmann, M., Howell, K., Kahlau, S.,
Radziejwoski, A., Russenberger, D., Rutishauser, D., Small, I.,
Stekhoven, D., Sulpice, R., Svozil, J., Wuyts, N., Stitt, M., Hilson, P.,
Granier, C. and Gruissem, W. (2012). Systems-based analysis of
Arabidopsis leaf growth reveals adaptation to water deficit. Molecular
Systems Biology 8: 606. Download
Hauser, A. and Bühlmann, P. (2012). Two optimal strategies for
active learning of causal models from interventions. Proc. of the 6th
European Workshop on Probabilistic Graphical Models (PGM 2012),
pp. 123-130, 2012. PDF
Hauser, A. and Bühlmann, P. (2012). Characterization and greedy
learning of interventional Markov equivalence classes of directed acyclic
graphs. Journal of Machine Learning Research 13, 2409-2464.
PDF
Kalisch, M., Mächler, M., Colombo, D., Maathuis, M.H. and
Bühlmann, P. (2012). Causal inference using graphical models with
the R package pcalg. Journal of Statistical Software 47 (11), 1-26. PDF
Stekhoven, D.J. and Bühlmann, P. (2012). MissForest -
nonparametric missing value imputation for mixed-type
data. Bioinformatics 28, 112-118. PDF
Städler, N. and Bühlmann, P. (2012). Missing values: sparse
inverse covariance estimation and an extension to sparse
regression. Statistics and Computing 22, 219-235. PDF
2011
Meinshausen, N., Maathuis, M.H. and Bühlmann,
P. (2011). Asymptotic optimality of the Westfall-Young permutation
procedure for multiple testing under dependence. Annals of
Statistics 39, 3369-3391.PDF
Bühlmann, P. and Cai, T. (2011). Introduction to the Lehmann
special section. Annals of Statistics 39, 2243.
Zhou, S., Rütimann, P., Xu, M. and Bühlmann,
P. (2011). High-dimensional covariance estimation based on Gaussian
graphical models. Journal of Machine Learning Research 12,
2975-3026.Abstract
and PDF
Bühlmann, P. (2011). Invited Discussion on "Adaptive confidence
intervals for the test error in classification (E.B. Laber and
S.A. Murphy)". Journal of the American Statistical Association 106,
916-918. PDF
van de Geer, S., Bühlmann, P. and Zhou, S. (2011). The adaptive
and the thresholded Lasso for potentially misspecified models (and a
lower bound for the Lasso). Electronic Journal of Statistics 5,
688-749. PDF
Schelldorfer, J., Bühlmann, P. and van de Geer,
S. (2011). Estimation for high-dimensional linear mixed-effects models
using L1-penalization. Scandinavian Journal of Statistics 38, 197-214. PDF
Bühlmann, P. (2011). Invited Discussion on "Regression shrinkage
and selection via the Lasso: a retrospective (R. Tibshirani)". Journal of
the Royal Statistical Society: Series B 73,
277-279. PDF
Buller, F., Steiner, M., Frey, K., Mircsof, D., Scheuermann, J.,
Kalisch, M., Bühlmann, P., Supuran, C.T., Neri, D. (2011). Selection
of carbonic anhydrase IX inhibitors from one million DNA-encoded
compounds. ACS Chemical Biology 6, 336-344.
2010
Bühlmann, P. (2010). Remembrance of Leo Breiman. Annals of
Applied Statistics 4, 1638-1641. PDF
Hothorn, T., Bühlmann, P., Kneib, T., Schmid M. and Hofner,
B. (2010). Model-based boosting 2.0. Journal of Machine Learning Research
11, 2109-2113. PDF
Meinshausen, N. and Bühlmann, P. (2010). Stability
selection (with discussion). Journal of the Royal Statistical Society:
Series B 72, 417-473. PDF
Städler, N., Bühlmann, P. and van de Geer,
S. (2010). l1-penalization for mixture regression models (with
discussion). TEST 19, 209-285. PDF.
Rejoinder
Gerster, S., Qeli, E., Ahrens, C.H. and Bühlmann,
P. (2010). Protein and gene model inference based on
statistical modeling in k-partite graphs. Proceedings of the National
Academy of Sciences USA 107, 12101-12106. PDF.
Supporting Information
Maathuis, M.H., Colombo, D., Kalisch, M. and Bühlmann,
P. (2010). Predicting causal effects in large-scale systems from
observational data. Nature Methods 7,
247-248. PDF. Supplementary
Material.
(See also the editorial
"Cause and effect" in the same issue: Nature Methods 7,
243. PDF)
Bühlmann, P., Kalisch, M. and Maathuis, M.H. (2010). Variable
selection in high-dimensional linear models: partially faithful
distributions and the PC-simple algorithm. Biometrika 97,
261-278. PDF
Dahinden, C., Kalisch, M. and Bühlmann, P. (2010). Decomposition
and model selection for large contingency tables. Biometrical Journal 52,
233-252. PDF
Bühlmann, P. and Hothorn, T. (2010). Twin Boosting: improved feature
selection and prediction. Statistics and Computing 20, 119-138. PDF
Kalisch, M., Fellinghauer, B.A.G., Grill, E., Maathuis, M.H.,
Mansmann, U., Bühlmann, P. and Stucki, G. (2010). Understanding
human functioning using graphical models. BMC Medical Research
Methodology 10:14,
1-10. Download
paper.
Dahinden, C., Ingold, B., Wild, P., Boysen, G., Luu, V.-D., Montani,
M., Kristiansen, G., Sulser, T., Bühlmann, P., Moch, H., Schraml,
P. (2010). Mining tissue microarray data to uncover combinations of
biomarker expression patterns that improve intermediate staging and
grading of clear cell renal cell cancer. Clinical Cancer Research 16,
88-98.
Bühlmann, P. and Yu, B. (2010). Boosting. Wiley
Interdisciplinary Reviews: Computational Statistics 2, 69-74. PDF
All publications
Peer reviewed articles
-
Bühlmann, P. (1994). Blockwise bootstrapped empirical processes for
stationary sequences. Annals of Statistics 22, 995-1012.
-
Bühlmann, P. (1995). The blockwise bootstrap for general empirical
processes of stationary sequences. Stochastic Processes and their
Applications 58, 247-265.
-
Bühlmann, P. and Künsch, H.R. (1995). The blockwise bootstrap for general
parameters of a stationary time series. Scandinavian Journal of Statistics
22, 35-54.
-
Bühlmann, P. (1995). Moving-average representation for
autoregressive approximations. Stochastic Processes and
their Applications 60, 331-342.
-
Bühlmann, P. (1996). Locally adaptive lag-window spectral
estimation. Journal Time Series Analysis 17, 247-270.
-
Bickel, P.J. and Bühlmann, P. (1996). What is a linear process? Proceedings
National Academy of Sciencies USA 93, 12128-12131.
-
Bühlmann, P. (1997). Sieve Bootstrap for Time Series. Bernoulli 3, 123-148.
-
Bickel, P.J. and Bühlmann, P. (1997). Closure of linear
processes. Journal of Theoretical Probability 10, 445-479.
-
Bühlmann, P. (1998). Extreme events from return-volume process: a
discretization approach for complexity reduction. Applied
Financial Economics 8, 267-278.
-
Bühlmann, P. (1998). Sieve bootstrap for smoothing in nonstationary time
series. Annals of Statistics 26, 48-83.
-
Bühlmann, P. (1999). Efficient and adaptive post-model-selection
estimators. Journal of Statistical Planning and Inference 79, 1-9.
-
Bühlmann, P. (1999). Dynamic adaptive partitioning for nonlinear time
series. Biometrika 86, 555-571. Extended version (compressed postscript).
-
Bühlmann, P. and Bühlmann, H. (1999). Selection of credibility regression
models. ASTIN Bulletin (Journal of the International Actuarial
Association) 29, 245-270.
-
Bühlmann, P. and Künsch, H.R. (1999). Invited Comment on "Prediction of Spatial
Cumulative Distribution Functions Using Subsampling (Lahiri, Kaiser,
Cressie and Hsu)". Journal of the American Statistical Association 94,
97-99.
-
Bühlmann, P. and Künsch, H.R. (1999). Block length selection in the
bootstrap for time series. Computational Statistics & Data Analysis 31,
295-310.
-
Bühlmann, P. and Wyner, A.J. (1999). Variable length Markov chains. Annals
of Statistics 27, 480-513.
-
Bickel, P.J. and Bühlmann, P. (1999). A new Mixing Notion and
Functional Central Limit Theorems for a Sieve Bootstrap in Time
Series. Bernoulli 5, 413-446.
-
Bühlmann, P. (2000). Von Daten zu stochastischen Modellen (in
German). Elemente der Mathematik 55, 1-18. Compressed postscript.
-
Bühlmann, P. (2000). Model selection for variable length Markov chains and
tuning the context algorithm. Annals of the Institute of
Statistical Mathematics 52, 287-315. Compressed postscript.
-
Bühlmann, P. and Yu, B. (2000). Invited Discussion on "Additive logistic
regression: a statistical view of boosting (Friedman, Hastie and
Tibshirani)". Annals of Statistics 28, 377-386. Compressed postscript. For original paper (Annals of Statistics 28, 337-407) click here.
-
Audrino, F. and Bühlmann, P. (2001). Tree-structured generalized
autoregressive conditional heteroscedastic models. Journal of the
Royal Statistical Society: Series B 63, 727-744.
Compressed postscript.
-
Bühlmann, P. (2002). Sieve bootstrap with variable length Markov chains for
stationary categorical time series (with discussion). Journal
of the American Statistical Association 97, 443-456.
Compressed postscript.
-
Bühlmann, P. (2002). Rejoinder of "Sieve bootstrap with variable length
Markov chains for stationary categorical time series". Journal of the
American Statistical Association 97, 466-471.
-
Bühlmann, P. (2002). Bootstraps for time series. Statistical
Science 17, 52-72.
Compressed postscript.
-
Ango Nze, P., Bühlmann, P. and Doukhan, P. (2002). Weak dependence beyond
mixing and asymptotics for nonparametric regression. Annals of
Statistics 30, 397-430.
Compressed postscript.
-
Bühlmann, P. and Yu, B. (2002). Analyzing bagging. Annals of Statistics 30,
927-961.
Compressed postscript.
-
Bühlmann, P. and McNeil, A.J. (2002). An algorithm for nonparametric GARCH
modelling. Computational Statistics & Data Analysis 40, 665-683.
Compressed postscript.
-
Dettling, M. and Bühlmann, P. (2002). Supervised clustering of
genes.Genome
Biology 3(12): research0069.1-0069.15. Software.
-
Audrino, F. and Bühlmann, P. (2003). Volatility estimation with functional
gradient descent for very high-dimensional financial time
series. Journal of Computational Finance Vol. 6, No. 3, 65-89.
PDF
-
Dettling, M. and Bühlmann, P. (2003). Boosting for tumor
classification with gene expression data. Bioinformatics 19, No. 9,
1061-1069.
Compressed postscript.
PDF. Software.
-
Bühlmann, P. and Yu, B. (2003). Boosting with the L2 loss: regression
and classification. Journal of the American Statistical
Association 98, 324-339.
PDF
-
Audrino, F. and Bühlmann, P. (2004). Synchronizing multivariate financial
time series. The Journal of Risk 6 (2), 81-106.
PDF
-
Bühlmann, P. and Yu, B. (2004). Invited Discussion on three papers on
boosting by Jiang, Lugosi and Vayatis, and Zhang. Annals of Statistics 32,
96-101.
PDF
-
Mächler, M. and Bühlmann, P. (2004). Variable length Markov chains:
methodology, computing and software. Journal of
Computational and Graphical Statistics 13, 435-455. Click here.
-
Dettling, M. and Bühlmann, P. (2004). Finding predictive gene groups
from microarray data. Journal of Multivariate Analysis 90, 106-131. Compressed
postscript.
PDF
-
Dettling, M. and Bühlmann, P. (2004). Volatility and risk estimation with
linear and nonlinear methods based on high frequency data. Applied
Financial Economics 14, 717-729. PDF.
- Teuffel, O., Dettling, M., Cario, G., Stanulla, M., Schrappe, M., Bühlmann,
P., Niggli, F. and Schäfer, B. (2004). Gene expression profiles and risk
stratification in childhood acute lymphoblastic leukemia. Haematologica 89,
801-808.
- Wachtel, M., Dettling, M., Koscielniak, E., Stegmaier, S., Treuner,
J., Simon-Klingenstein, K., Bühlmann, P., Niggli, F. and Schäfer,B. (2004).
Gene expression signatures identify rhabdomyosarcoma subtypes and detect a
novel t(2;2)(q35;p23) translocation fusing PAX3 to NCOA1. Cancer Research
64, 5539-5545.
-
Wille, A., Zimmermann, P., Vranova, E., Fürholz, A., Laule, O., Bleuler,
S., Hennig, L., Prelic, A., von Rohr, P., Thiele, L., Zitzler, E.,
Gruissem, W. and Bühlmann, P. (2004). Sparse graphical Gaussian modeling
of the isoprenoid gene network in Arabidopsis thaliana.Genome Biology
5(11) R92, 1-13.
-
Meinshausen, N. and Bühlmann, P. (2005). Lower bounds for the number of
false null hypotheses for multiple testing of associations under general
dependence structures. Biometrika 92, 893-907.
PDF
-
Wille, A. and Bühlmann, P. (2006). Low-order conditional independence
graphs for inferring genetic networks. Statistical
Applications in Genetics and Molecular Biology 5 (1) Art1, 1-32.Download paper.
- Prelic, A., Bleuler, S., Zimmermann, P., Wille, A., Bühlmann, P.,
Gruissem, W., Hennig, L., Thiele, L. and Zitzler, E. (2006). A systematic
comparison and evaluation of biclustering methods for gene expression
data. Bioinformatics 22, 1122-1129. Download paper.
BicAT: A Biclustering Analysis Toolbox.
-
Bühlmann, P. (2006). Boosting for high-dimensional linear models. Annals of
Statistics 34, 559-583.
PDF
-
Lutz, R.W. and Bühlmann, P. (2006). Boosting for high-multivariate
responses in high-dimensional linear regression. Statistica Sinica 16,
471-494.
PDF
-
Lutz, R.W. and Bühlmann, P. (2006). Conjugate direction boosting. Journal
of Computational and Graphical Statistics 15, 287-311.
PDF
- Bühlmann, P. and Yu, B. (2006). Sparse Boosting. Journal of Machine
Learning Research 7, 1001-1024.
PDF
-
Hothorn, T., Bühlmann, P., Dudoit, S., Molinaro, A. and van der Laan,
M. (2006). Survival ensembles. Biostatistics 7, 355-373.Download paper.
-
Meinshausen, N. and Bühlmann, P. (2006). High-dimensional graphs and
variable selection with the Lasso. Annals of Statistics 34, 1436-1462.
PDF. According to Essential Science Indicators, this has been selected
as New Hot Paper.
- Hothorn, T. and Bühlmann, P. (2006). Model-based boosting in high
dimensions. Bioinformatics 22, 2828-2829. PDF
- Goeman, J.J. and Bühlmann, P. (2007). Analyzing gene expression data
in terms of gene sets: methodological issues. Bioinformatics 23, 980-987. PDF
- Kalisch, M. and Bühlmann, P. (2007). Estimating high-dimensional
directed acyclic graphs with the PC-algorithm. Journal of Machine
Learning Research 8, 613-636.
PDF
- Elsener, A., Samson, C.C.M., Brändle, M.P., Bühlmann, P. and Lüthi,
H.P. (2007). Statistical analysis of quantum chemical data using
generalized XML/CML archives for the derivation of molecular design
rules. Chimia 61, 165-168. PDF
- Wille, A., Gruissem, W., Bühlmann, P. and Hennig, L. (2007). EVE
(External Variance Estimation) increases statistical power for detecting
differentially expressed genes. The Plant Journal 52, 561-569.PDF
- Bühlmann, P. (2007). Bootstrap schemes for time series (in
Russian). Quantile 3, 37-56.PDF
- Meier, L. and Bühlmann, P. (2007). Smoothing L1-penalized estimators
for high-dimensional time-course data. Electronic Journal of
Statistics 1, 597-615.
PDF
- Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms:
regularization, prediction and model fitting (with
discussion). Statistical Science
22, 477-505. (The paper includes supporting software). PDF
- Bühlmann, P. and Hothorn, T. (2007). Rejoinder of "Boosting algorithms:
regularization, prediction and model
fitting". Statistical Science
22, 516-522. PDF
- Dahinden, C., Parmigiani, G., Emerick, M.C. and Bühlmann,
P. (2007). Penalized likelihood for sparse contingency tables with an
application to full-length cDNA libraries. BMC Bioinformatics 2007,
8:476, 1-11.
- Meier, L., van de Geer, S. and Bühlmann, P. (2008). The Group Lasso
for logistic regression. Journal of the Royal Statistical Society: Series
B, 70, 53-71.
PDF
- Schöner, D., Kalisch, M., Leisner, C., Meier, L., Sohrmann, M., Faty,
M., Barral, Y., Peter, M., Gruissem, W. and Bühlmann,
P. (2008). Annotating novel genes by integrating synthetic lethals and
genomic information. BMC Systems Biology 2008,
2:3, 1-14.
- Bühlmann, P. and Yu, B. (2008). Invited Discussion on "Evidence
contrary to the statistical view of boosting (D. Mease and A. Wyner)".
Journal of Machine Learning Research 9,
187-194. Download paper with discussion.
-
Lutz, R.W., Kalisch, M. and Bühlmann, P. (2008). Robustified
L2 boosting. Computational Statistics & Data Analysis 52, 3331-3341.
PDF
-
Meinshausen, N. and Bühlmann, P. (2008). Invited Discussion on "Treelets -
An adaptive multi-scale basis for sparse unordered data (A.B. Lee,
B. Nadler and L. Wasserman)". Annals of Applied
Statistics 2, 478-481. PDF
- Bühlmann, P. and Meier, L. (2008). Invited Discussion on "One-step
sparse estimates in nonconcave penalized likelihood models (H. Zou and
R. Li)". Annals of Statistics 36,
1534-1541. PDF
- Lange, V., Malmström, J. A., Didion, J., King, N. L., Johansson,
B. P., Schäfer, J., Rameseder, J., Wong, C.-H., Deutsch, E. W., Brusniak,
M.-Y., Bühlmann, P., Björck, L., Domon, B. and Aebersold,
R. (2008). Targeted quantitative analysis of Streptococcus pyogenes
virulence factors by multiple reaction monitoring. Molecular
& Cellular Proteomics 7, 1489-1500.
Download paper.
-
Bühlmann, P. (2008). Invited Discussion on "Sure Independence Screening
(J. Fan and J. Lv)". Journal of the Royal Statistical Society: Series B
70, 884-887.
PDF
- Kalisch, M. and Bühlmann, P. (2008). Robustification of the
PC-algorithm for directed acyclic graphs. Journal of Computational and
Graphical Statistics 17, 773-789.
PDF
- Audrino, F. and Bühlmann, P. (2009). Splines for financial
volatility. Journal of the Royal Statistical Society: Series B 71,
655-670.
PDF
- Maathuis, M.H., Kalisch, M. and Bühlmann, P. (2009). Estimating
high-dimensional intervention effects from observational data. Annals of
Statistics 37, 3133-3164.PDF
- Meier, L., van de Geer, S. and Bühlmann, P. (2009). High-dimensional
additive modeling. Annals of Statistics 37,
3779-3821. PDF
- Buller, F., Zhang, Y., Scheuermann, J., Schäfer, J., Bühlmann, P. and
Neri, D. (2009). Discovery of TNF inhibitors from a DNA-encoded chemical
library based on Diels-Alder cycloaddition. Chemistry & Biology 16,
1075-1086.
- Rütimann, P. and Bühlmann, P. (2009). High dimensional sparse covariance
estimation via directed acyclic graphs. Electronic Journal of Statistics 3,
1133-1160. PDF
- van de Geer, S. and Bühlmann, P. (2009). On the conditions used to
prove oracle results for the Lasso. Electronic Journal
of Statistics 3,
1360-1392. PDF
- Meinshausen, N., Meier, L. and Bühlmann, P. (2009). p-values for
high-dimensional regression. Journal of the American
Statistical Association 104, 1671-1681. PDF
-
Bühlmann, P. and Yu, B. (2010). Boosting. Wiley
Interdisciplinary Reviews: Computational Statistics 2, 69-74. PDF
- Dahinden, C., Ingold, B., Wild, P., Boysen, G., Luu, V.-D., Montani,
M., Kristiansen, G., Sulser, T., Bühlmann, P., Moch, H., Schraml,
P. (2010). Mining tissue microarray data to uncover combinations of
biomarker expression patterns that improve intermediate staging and
grading of clear cell renal cell cancer. Clinical Cancer Research 16,
88-98.
- Kalisch, M., Fellinghauer, B.A.G., Grill, E., Maathuis, M.H.,
Mansmann, U., Bühlmann, P. and Stucki, G. (2010). Understanding
human functioning using graphical models. BMC Medical Research
Methodology 10:14,
1-10. Download
paper.
- Bühlmann, P. and Hothorn, T. (2010). Twin Boosting: improved feature
selection and prediction. Statistics and Computing 20, 119-138. PDF
- Maathuis, M.H., Colombo, D., Kalisch, M. and Bühlmann,
P. (2010). Predicting causal effects in large-scale systems from
observational data. Nature Methods 7,
247-248. PDF. Supplementary
Material.
(See also the editorial
"Cause and effect" in the same issue: Nature Methods 7,
243. PDF)
- Bühlmann, P., Kalisch, M. and Maathuis, M.H. (2010). Variable
selection in high-dimensional linear models: partially faithful
distributions and the PC-simple algorithm. Biometrika 97,
261-278. PDF
- Dahinden, C., Kalisch, M. and Bühlmann, P. (2010). Decomposition
and model selection for large contingency tables. Biometrical Journal 52,
233-252. PDF
- Gerster, S., Qeli, E., Ahrens, C.H. and Bühlmann,
P. (2010). Protein and gene model inference based on
statistical modeling in k-partite graphs. Proceedings of the National
Academy of Sciences USA 107, 12101-12106. PDF.
Supporting
Information
- Städler, N., Bühlmann, P. and van de Geer,
S. (2010). l1-penalization for mixture regression models (with
discussion). TEST 19, 209-285. PDF.
Rejoinder
- Meinshausen, N. and Bühlmann, P. (2010). Stability
selection (with discussion). Journal of the Royal Statistical Society:
Series B 72, 417-473. PDF
- Hothorn, T., Bühlmann, P., Kneib, T., Schmid M. and Hofner,
B. (2010). Model-based boosting 2.0. Journal of Machine Learning Research
11, 2109-2113. PDF
- Bühlmann, P. (2010). Remembrance of Leo Breiman. Annals of
Applied Statistics 4,
1638-1641. PDF
- Buller, F., Steiner, M., Frey, K., Mircsof, D., Scheuermann, J.,
Kalisch, M., Bühlmann, P., Supuran, C.T., Neri, D. (2011). Selection
of carbonic anhydrase IX inhibitors from one million DNA-encoded
compounds. ACS Chemical Biology 6, 336-344.
- Bühlmann, P. (2011). Invited Discussion on "Regression shrinkage
and selection via the Lasso: a retrospective (R. Tibshirani)". Journal of
the Royal Statistical Society: Series B 73,
277-279. PDF
- Schelldorfer, J., Bühlmann, P. and van de Geer,
S. (2011). Estimation for high-dimensional linear mixed-effects models
using L1-penalization. Scandinavian Journal of Statistics 38, 197-214. PDF
- van de Geer, S., Bühlmann, P. and Zhou, S. (2011). The adaptive
and the thresholded Lasso for potentially misspecified models (and a
lower bound for the Lasso). Electronic Journal of Statistics 5,
688-749. PDF
- Bühlmann, P. (2011). Invited Discussion on "Adaptive confidence
intervals for the test error in classification (E.B. Laber and
S.A. Murphy)". Journal of the American Statistical Association 106,
916-918. PDF
- Zhou, S., Rütimann, P., Xu, M. and Bühlmann,
P. (2011). High-dimensional covariance estimation based on Gaussian
graphical models. Journal of Machine Learning Research 12,
2975-3026.Abstract
and PDF
- Bühlmann, P. and Cai, T. (2011). Introduction to the Lehmann
special section. Annals of Statistics 39, 2243.
- Meinshausen, N., Maathuis, M.H. and Bühlmann,
P. (2011). Asymptotic optimality of the Westfall-Young permutation
procedure for multiple testing under dependence. Annals of
Statistics 39,
3369-3391.PDF
- Städler, N. and Bühlmann, P. (2012). Missing values: sparse
inverse covariance estimation and an extension to sparse
regression. Statistics and Computing 22,
219-235. PDF
- Stekhoven, D.J. and Bühlmann, P. (2012). MissForest -
nonparametric missing value imputation for mixed-type
data. Bioinformatics 28,
112-118. PDF
- Kalisch, M., Mächler, M., Colombo, D., Maathuis, M.H. and
Bühlmann, P. (2012). Causal inference using graphical models with
the R package pcalg. Journal of Statistical Software 47 (11),
1-26. PDF
- Hauser, A. and Bühlmann, P. (2012). Characterization and greedy
learning of interventional Markov equivalence classes of directed acyclic
graphs. Journal of Machine Learning Research 13, 2409-2464.
PDF
- Hauser, A. and Bühlmann, P. (2012). Two optimal strategies for
active learning of causal models from interventions. Proc. of the 6th
European Workshop on Probabilistic Graphical Models (PGM 2012),
pp. 123-130, 2012. PDF
- Baerenfaller, K., Massonnet, C., Walsh, S., Baginsky, S., Bühlmann,
P., Hennig, L., Hirsch-Hoffmann, M., Howell, K., Kahlau, S.,
Radziejwoski, A., Russenberger, D., Rutishauser, D., Small, I.,
Stekhoven, D., Sulpice, R., Svozil, J., Wuyts, N., Stitt, M., Hilson, P.,
Granier, C. and Gruissem, W. (2012). Systems-based analysis of
Arabidopsis leaf growth reveals adaptation to water deficit. Molecular
Systems Biology 8: 606. Download
- Beleut, M., Zimmermann, P., Baudis, M., Bruni, N., Bühlmann, P.,
Laule, O., Luu, V.-D., Gruissem, W., Schraml, P. and Moch,
H. (2012). Integrative genome-wide expression profiling identifies three
distinct molecular subgroups of renal cell carcinoma with different patient
outcome. BMC Cancer 12:310. Download
- Stekhoven, D.J., Moraes, I., Sveinbjörnsson, G., Hennig, L.,
Maathuis, M.H. and Bühlmann, P. (2012). Causal stability
ranking. Bioinformatics 28,
2819-2823. PDF. Supplementary
Data
- Fellinghauer, B., Bühlmann, P., Ryffel, M., von Rhein, M.,
Reinhardt, J.D. (2013). Stable graphical model estimation with Random
Forests for discrete, continuous, and mixed
variables. Computational Statistics & Data
Analysis 64,
132-152. PDF
- Uhler, C., Raskutti, G., Bühlmann, P. and Yu, B. (2013). Geometry
of faithfulness assumption in causal inference. Annals of
Statistics 41, 436-463. PDF
- van de Geer, S. and Bühlmann, P. (2013). l0-penalized maximum
likelihood for sparse directed acyclic graphs. Annals of
Statistics 41,
536-567. PDF
- Bühlmann, P. (2013). Causal statistical inference in high
dimensions. Mathematical Methods of Operations Research 77,
357-370.PDF
- Bühlmann, P. (2013). Statistical significance in high-dimensional
linear models. Bernoulli 19, 1212-1242. PDF
- Bühlmann, P., Rütimann, P., van de Geer, S. and Zhang,
C.-H. (2013). Correlated variables in regression: clustering and sparse
estimation (with discussion). Journal of Statistical Planning and
Inference 143, 1835-1871. PDF.
Rejoinder
- Bühlmann, P., Rütimann, P. and Kalisch,
M. (2013). Controlling false positive selections in high-dimensional
regression and causal inference. Statistical Methods in
Medical Research 22, 466-492. PDF
- Hothorn, T., Kneib, T. and Bühlmann, P. (2014). Conditional
transformation models. Journal of the Royal Statistical
Society: Series B 76, 3-37. PDF
- Bühlmann, P., Kalisch, M. and Meier, L. (2014). High-dimensional
statistics with a view towards applications in biology. Annual Review of
Statistics and its Applications 1, 255-278. Download
- Gerster, S., Kwon, T., Ludwig, C., Matondo, M., Vogel, C.,
Marcotte, E., Aebersold, R. and Bühlmann, P. (2014). Statistical
approach to protein quantification. Molecular and Cellular
Proteomics 13, 666-677. Download
- Kalisch, M. and Bühlmann, P. (2014). Causal structure learning
and inference: a selective review. Quality Technology & Quantitative
Management 11, 3-21. Download
- Peters, J. and Bühlmann, P. (2014). Identifiability of Gaussian
structural equation models with equal error variances. Biometrika 101,
219-228. PDF
- Hauser, A. and Bühlmann, P. (2014). Two optimal strategies for
active learning of causal models from interventional data. International
Journal of Approximate Reasoning 55, 926-939. (This is a longer
paper version of Hauser and Bühlmann (PGM 2012)).PDF
- Schelldorfer, J., Meier, L. and Bühlmann, P. (2014). GLMMLasso:
An algorithm for high-dimensional generalized linear mixed models
using L1-penalization. Journal of Computational and Graphical
Statistics 23, 460-477.PDF
- Bühlmann, P. (2014). Invited Discussion of Big Bayes stories and
BayesBag. Statistical Science 29, 91-94.PDF
- Bühlmann, P., Meier, L. and van de Geer, S. (2014). Invited Discussion on
"A significance test for the Lasso (R. Lockhart, J. Taylor,
R. Tibshirani and R. Tibshirani)". Annals of
Statistics 42, 469-477.PDF
- Bühlmann, P. and Mandozzi, J. (2014). High-dimensional variable
screening and bias in subsequent inference, with an empirical
comparison. Computational Statistics 29, 407-430.PDF
- van de Geer, S., Bühlmann, P., Ritov, Y. and Dezeure, R. (2014). On
asymptotically optimal confidence regions and tests for high-dimensional
models. Annals of Statistics 42, 1166-1202. PDF
- Städler, N., Stekhoven, D.J. and Bühlmann,
P. (2014). Pattern alternating maximization algorithm for missing data in
large p, small n problems. Journal of Machine Learning Research 15, 1903-1928.PDF
- Lin, S., Uhler, C., Sturmfels, B. and Bühlmann,
P. (2014). Hypersurfaces and their singularities in partial correlation
testing. Foundations of Computational Mathematics 14, 1079-1116. PDF
- Loh, P. and Bühlmann, P. (2014). High-dimensional learning of
linear causal networks via inverse covariance estimation. Journal of
Machine Learning Research 15, 3065-3105. PDF
- Bühlmann, P., Peters, J. and Ernest, J. (2014). CAM: Causal
Additive Models, high-dimensional order search and penalized
regression. Annals of Statistics 42, 2526-2556. PDF
- Bühlmann, P. (2014). Invited Discussion on "The Evolution of Boosting
Algorithms" and "Extending Statistical Boosting" (A. Mayr, H. Binder,
O. Gefeller and M. Schmid). Methods of Information in Medicine 53,
436-437.PDF
- Rämö, P., Drewek, A., Arrieumerlou, C.,
Beerenwinkel, N., Ben-Tekaya, H., Cardel, B., Casanova, A.,
Conde-Alvarez, R., Cossart, P., Csucs, G., Eicher, S., Emmenlauer, M.,
Greber, U., Hardt, W.-D., Helenius, A., Kasper, C., Kaufmann, A.,
Kreibich, S., Kühbacher, A., Kunszt, P., Low, S.H., Mercer, J.,
Mudrak, S., Muntwiler, S., Pelkmans, L., Pizarro-Cerda, J., Podvinec, M.,
Pujadas, E., Rinn, B., Rouilly, V., Schmich, F., Siebourg-Polster, J.,
Snijder, B., Stebler, M., Studer, G., Szczurek, E., Truttmann, M., von
Mering, C., Vonderheit, A., Yakimovich, A., Bühlmann, P. and Dehio,
C. (2014). Simultaneous analysis of large-scale RNAi screens for pathogen
entry. BMC Genomics
15:1162. Download
- Hauser, A. and Bühlmann, P. (2015). Jointly interventional and
observational data: estimation of interventional Markov equivalence
classes of directed acyclic graphs. Journal of the Royal
Statistical Society: Series B 77, 291-318. PDF
- Peters, J. and Bühlmann, P. (2015). Structural intervention
distance (SID) for evaluating causal graphs. Neural Computation
27,771-799. PDF
- Meinshausen, N. and Bühlmann, P. (2015). Maximin effects in
inhomogeneous large-scale data. Annals of Statistics 43, 1801-1830. PDF
- Bühlmann, P. and van de Geer, S. (2015). High-dimensional inference
in misspecified linear models. Electronic Journal of
Statistics 9,
1449-1473. Download
- Dezeure, R., Bühlmann, P., Meier, L. and Meinshausen,
N. (2015). High-dimensional inference: confidence intervals, p-values and
R-software hdi. Statistical Science 30, 533-558. PDF
- Ernest, J. and Bühlmann, P. (2015). Marginal integration for
nonparametric causal inference. Electronic Journal of
Statistics 9, 3155-3194. Download
- Ruiz-Sola, M.A., Coman, D., Beck, G., Barjaa, M.V., Colinas-Martinez,
M., Graf, A., Welsch, R., Rütimann, P., Bühlmann, P.,
Bigler, L., Gruissem, W., Rodriguez-Concepcion, M. and Vranova,
E. (2016). Arabidopsis geranylgeranyl diphosphate synthase 11 is a hub
isozyme required for the production of most photosynthesis-related
isoprenoids. New Phytologist 209, 252-264.
- Bühlmann, P. and Meinshausen, N. (2016). Magging: maximin
aggregation for inhomogeneous large-scale data. Proceedings of the IEEE
104, 126-135. PDF
- Mandozzi, J. and Bühlmann, P. (2016). Hierarchical testing in the
high-dimensional setting with correlated
variables. Journal of the American Statistical Association 111,
331-343. PDF. Supplement
- Bühlmann, P. and Leonardi, F. (2016). Comments on: A random
forest guided tour. (Discussion on a paper by G. Biau and
E. Scornet). TEST 25, 239-246. PDF
- Nowzohour, C. and Bühlmann, P. (2016). Score-based causal
learning in additive noise models. Statistics 50, 471-485. PDF
- Mandozzi, J. and Bühlmann, P. (2016). A sequential rejection
testing method for high-dimensional regression with correlated
variables. International Journal of Biostatistics 12, 79-95. PDF
- Meinshausen, N., Hauser, A., Mooij, J.M., Peters, J., Versteeg, P. and
Bühlmann, P. (2016). Methods for causal inference from gene
perturbation experiments and validation. Proceedings of
the National Academy of Sciences USA 113,
7361-7368. PDF. Supporting Information
- Bühlmann, P. and Dezeure, R. (2016). Invited Discussion on
"Regularized regression for categorical data (Tutz and
Gertheiss)". Statistical Modelling: An International Journal 16, 205-211. PDF
- Buzdugan, L., Kalisch, M., Navarro, A., Schunk, D., Fehr, E. and
Bühlmann, P. (2016). Assessing statistical significance in
multivariable genome wide association analysis. Bioinformatics 32,
1990-2000. Download
- Peters, J., Bühlmann, P. and Meinshausen, N. (2016). Causal
inference using invariant prediction: identification and confidence
intervals (with discussion). Journal of the Royal Statistical Society,
Series B 78, 947-1012. PDF
- Klasen, J.R., Barbez, E., Meier, L., Meinshausen, N., Bühlmann,
P., Koornneef, M., Busch, W. and Schneeberger, K. (2016). A multi-marker
association method for Genome-Wide Association studies without the need
for population structure correction. Nature Communications 7, Article
number 13299 (2016), doi:10.1038/ncomms13299. Download
- Li, S., Ernest, J. and Bühlmann, P. (2017). Nonparametric causal
inference from observational time series through marginal
integration. Econometrics and Statistics 2,
81-105. PDF
- Bühlmann, P. (2017). High-dimensional statistics, with applications
to genome-wide association studies. EMS Surveys in Mathematical
Sciences 4, 45-75. Preprint PDF
- Dezeure, R., Bühlmann, P. and Zhang,
C.-H. (2017). High-dimensional simultaneous inference with the
bootstrap (with discussion). TEST 26,
685-719. Download
- Dezeure, R., Bühlmann, P. and Zhang,
C.-H. (2017). Rejoinder on: High-dimensional simultaneous inference with the
bootstrap. TEST 26,
751-758. Download
- Nowzohour, C., Maathuis, M.H., Evans, R.J. and Bühlmann,
P. (2017). Distributional equivalence and structure learning for bow-free
acyclic path diagrams. Electronic Journal of Statistics 11, 5342-5374. Download
- Pfister, N., Bühlmann, P., Schölkopf, B. and Peters,
J. (2018). Kernel-based tests for joint independence. Journal of the
Royal Statistical Society, Series B 80,
5-31. Download
- Shah, R.D. and Bühlmann, P. (2018). Goodness of fit tests for
high-dimensional linear models. Journal of the Royal Statistical Society,
Series B 80, 113-135. Download
- Hothorn, T., Möst, L. and Bühlmann, P. (2018). Most likely
transformations. Scandinavian Journal of Statistics 45, 110-134. Download
- Bühlmann, P. and van de Geer, S. (2018). Statistics for big data:
A perspective. Statistics & Probability Letters 136, 37-41. Download
- Rothenhäusler, D., Ernest, J. and Bühlmann,
P. (2018). Causal inference in partially linear structural equation
models. Annals of Statistics 46, 2904-2938. Download
- Pfister, N., Bühlmann, P. and Peters, J. (2019). Invariant causal
prediction for sequential data. Journal of the American
Statistical Association 114, 1264-1276.Download
- Rothenhäusler, D., Bühlmann, P. and Meinshausen,
N. (2019). Causal Dantzig: fast inference in linear structural equation
models with hidden variables under additive interventions. Annals of
Statistics 47, 1688-1722. Download
- Bühlmann, P. (2019). Comments on: Data science, big data and
statistics. TEST 28, 330-333. Download
- Pfister, N., Weichwald, S., Bühlmann, P. and Schölkopf,
B. (2019). Robustifying Independent Component Analysis by adjusting for
group-wise stationary noise. Journal of Machine Learning Research 20,
(147):1-50. Download
- Renaux, C., Buzdugan, L., Kalisch, M. and Bühlmann,
P. (2020). Hierarchical inference for genome-wide association studies: a
view on methodology with software (with discussion). Computational
Statistics 35, 1-40. Download
- Renaux, C., Buzdugan, L., Kalisch, M. and Bühlmann,
P. (2020). Rejoinder on: Hierarchical inference for genome-wide
association studies: a view on methodology with software. Computational
Statistics 35, 59-67. Download
- Bühlmann, P. (2020). Invariance, Causality and
Robustness (with discussion). Statistical Science 35, 404-426. Download
- Bühlmann, P. (2020). Rejoinder: Invariance, Causality and
Robustness. Statistical Science 35,
434-436. Download
- Janková, J., Shah, R.D., Bühlmann, P. and Samworth
R.J. (2020). Goodness-of-fit testing in high-dimensional generalized
linear models. Journal of the Royal Statistical Society, Series
B 82, 773-795. Download
- Bühlmann, P. (2020). Toward causality and improving external
validity. Proceedings of the National Academy of Sciences
USA 117,
25963-25965. Download
- Chen, Y., Taeb, A. and Bühlmann, P. (2020). A look at robustness
and stability of l1- versus l0-regularization: Discussion of
papers by Bertsimas et al. and Hastie et al. Statistical Science 35,
614-622. Download
- Kovács, S., Li, H. and Bühlmann, P. (2020). Seeded
intervals and noise level estimation in change point detection: A
discussion of Fryzlewicz (2020). Journal of the Korean
Statistical Society 49, 1081-1089. Download
- Ćevid, D., Bühlmann, P. and Meinshausen,
N. (2020). Spectral deconfounding via perturbed sparse linear
models. Journal of Machine Learning Research 21, (232): 1-41. Download
- Bühlmann, P. and Ćevid, D. (2020). Deconfounding and
causal regularization for stability and external validity. International
Statistical Review 88, S114-S134. Download
- Schultheiss, C., Renaux, C. and Bühlmann, P. (2021). Multicarving
for high-dimensional post-selection inference. Electronic Journal of
Statistics 15, 1695-1742. Download
- Rothenhäusler, D., Meinshausen, N., Bühlmann, P. and Peters,
J. (2021). Anchor regression: heterogeneous data meet
causality. Journal of the Royal Statistical Society,
Series B 83, 215-246. Download
- Bühlmann, P. (2021). One modern culture of statistics. Comments
on Statistical Modeling: The Two Cultures (Breiman, 2001b). Observational
Studies 7, 33-40. Download
- Londschien, M., Kovács, S. and Bühlmann, P. (2021). Change
point detection for graphical models in presence of missing
values. Journal of Computational and Graphical Statistics 30, 768-779. Download
- Pfister, N., Williams, E.G., Peters, J., Aebersold, R. and Bühlmann,
P. (2021). Stabilizing variable selection and regression. Annals of
Applied Statistics 15,
1220-1246. Download
- Chen, Y. and Bühlmann, P. (2021). Domain adaptation under
structural causal models. Journal of Machine Learning Research 22, (261):
1-80. Download
- Emmenegger, C. and Bühlmann, P. (2021). Regularizing double
machine learning in partially linear endogenous models. Electronic
Journal of Statistics 15, 6461-6543. Download
- Guo, Z., Renaux, C., Bühlmann, P. and Cai, T.T. (2021). Group
inference in high dimensions with applications to hierarchical
testing. Electronic Journal of Statistics 15, 6633-6676. Download
- Williams, E.G., Pfister, N., Roy, S., Statzer, C., Ingels, J., Bohl,
C., Hasan, M., Cuklina, J., Bühlmann, P., Zamboni, N., Lu, L.,
Ewald, C.Y., Williams, R.W. and Aebersold, R. (2022). Multiomic profiling of the liver across diets and age in a diverse mouse population. Cell Systems 13,
43-57. Download Preprint bioRxiv:2020.08.20.222968
- Jablonski, K.P., Pirkl, M., Ćevid, D., Bühlmann, P. and
Beerenwinkel, N. (2022). Identifying cancer pathway dysregulations using
differential causal effects. Bioinformatics 38,
1550-1559. Download
- Kook, L., Sick, B. and Bühlmann, P. (2022). Distributional anchor
regression. Statistics and Computing 32: 39,
1-19. Download
- Jakobsen, M.E., Shah, R.D., Bühlmann, P. and Peters,
J. (2022). Structure learning for directed trees. Journal of Machine
Learning Research 23, (159): 1-97.Download
- Guo, Z., Ćevid, D. and Bühlmann, P. (2022). Doubly
Debiased Lasso: high-dimensional inference under hidden
confounding. Annals of Statistics 50, 1320-1347. Download
- Scheidegger, C., Hörrmann, J. and Bühlmann, P. (2022). The
weighted generalised covariance measure. Journal of
Machine Learning Research 23, (273): 1-68. Download
- Ćevid, D., Michel, L., Näf, J., Bühlmann,
P. and Meinshausen, N. (2022). Distributional Random Forests:
heterogeneity adjustment and multivariate distributional
regression. Journal of Machine Learning Research 23, (333): 1-79. Download
- Marmolejo-Ramos, F., Tejo, M., Brabec, M., Kuzilek, J., Joksimovic,
S., Kovanovic, V., González, J., Kneib, T., Bühlmann, P., Kook,
L., Briseño-Sánchez, G., Ospina,
R. (2023). Distributional regression modeling via generalized additive
models for location, scale, and shape: An overview through a dataset
from learning analytics. WIREs Data Mining and Knowledge Discovery 13,
e1479, 1-22. Download
- Danielli, S.G., Porpiglia, E., De Micheli, A.J., Navarro, N.,
Zellinger, M.J., Bechtold, I., Kisele, S., Volken, L., Marques, J.G.,
Kasper, S., Bode, P.K., Henssen, A.G., Gürgen, D., Delattre, O.,
Surdez, D., Roma, J., Bühlmann, P., Blau, H.M., Wachtel, M. and
Schäfer, B.W. (2023). Single-cell profiling of alveolar
rhabdomyosarcoma reveals RAS pathway inhibitors as cell-fate
hijackers with therapeutic relevance. Science Advances 9,
eade9238, 1-20. Download
- Kovács, S., Bühlmann, P., Li, H. and Munk,
A. (2023). Seeded binary segmentation: a general methodology for fast and
optimal change point detection. Biometrika 110,
249-256. Download
- Shah, R.D. and Bühlmann, P. (2023). Double-estimation-friendly
inference for high-dimensional misspecified models. Statistical Science
38,
68-91. Download
- Schultheiss, C. and Bühlmann, P. (2023). On the pitfalls of
Gaussian likelihood scoring for causal discovery. Journal of Causal
Inference 11(1), jci-2022-0068, 1-11. Download
- Bennett, N., Plecko, D., Ukor, I.-F., Meinshausen, N. and
Bühlmann, P. (2023). ricu: R's interface to intensive care
data. GigaScience 12, giad041,
1-8. Download
- Moor, M., Bennett, N., Plecko, D., Horn, M., Rieck, B., Meinshausen,
N., Bühlmann, P. and Borgwardt, K. (2023). Predicting sepsis using
deep learning across international sites: a retrospective development and
validation study. eClinicalMedicine 62, Article 103124, 1-13. Download
- Londschien, M., Bühlmann, P. and Kovács,
S. (2023). Random Forests for change point detection. Journal of Machine
Learning Research 24, (216):
1-45. Download
- Rothenhäusler, D. and Bühlmann, P. (2023). Distributionally
robust and generalizable inference. Statistical
Science, 38, No. 4, 527-542. Download
- Law, M. and Bühlmann, P. (2023). Discussion of “A Scale-Free
Approach for False Discovery Rate Control in Generalized Linear Models
(Dai, Lin, Xing and Liu)”. Journal of the American
Statistical Association 118, 1578-1583. Download
- Emmenegger, C. and Bühlmann, P. (2023). Plug-in machine
learning for partially linear mixed-effects models with repeated
measurements. Scandinavian Journal of Statistics 50, 1553-1567. Download
- Schultheiss, C. and Bühlmann, P. (2023). Ancestor regression in linear structural equation models. Biometrika 110, 1117-1124. Download
- Immer, A., Schultheiss, C., Vogt, J.E., Schölkopf, B.,
Bühlmann, P. and Marx, A. (2023). On the identifiability and
estimation of causal location-scale noise models. Proceedings of the
40th International Conference on Machine Learning (ICML), PMLR
202:14316-14332,
2023. Download
- Näf, J., Emmenegger, C., Bühlmann, P. and Meinshausen,
N. (2023). Confidence and uncertainty assessment for distributional
Random Forests. Journal of Machine Learning Research 24, (366): 1-77. Download
- Taeb, A., Bühlmann, P. and Chandrasekaran, V. (2024). Model
selection over partially ordered sets. Proceedings of the National
Academy of Sciences USA 121, No. 8, e314228121:
1-12. Download
- Schultheiss, C. and Bühlmann, P. (2024). Assessing the overall
and partial causal well-specification of nonlinear additive noise
models. Journal of Machine Learning Research 25, (159): 1-41. Download
- Schultheiss, C., Bühlmann, P. and Yuan, M. (2024). Assessing the
goodness of fit of linear regression via higher-order least
squares. Journal of the American Statistical Association 119, 1019-1031. Download
- Carl, D., Emmenegger, C., Bühlmann, P. and Guo, Z. (2023). TSCI:
two stage curvature identification for causal inference with invalid
instruments. To appear in the Journal of Statistical
Software. Preprint arXiv:2304.00513
- Kovács, S., Li, H., Haubner, L., Munk, A. and Bühlmann,
P. (2020). Optimistic search strategy: Change point detection for
large-scale data via adaptive logarithmic queries. To appear in the
Journal of Machine Learning
Research. Preprint arXiv:2010.10194
- Taeb, A., Gamella, J.L., Heinze-Deml, C. and Bühlmann,
P. (2021). Learning and scoring Gaussian latent variable causal models
with unknown additive interventions. To appear in the Journal of Machine
Learning
Research. Preprint arXiv:2101.06950
- Henzi, A., Shen, X., Law, M. and Bühlmann, P. (2023). Invariant
probabilistic prediction. To appear in Biometrika. Preprint arXiv:2309.10083
- Qian, L., Sun, R., Aebersold, R., Bühlmann, P., Sander,
C. and Guo, T. (2024). AI-empowered perturbation proteomics for complex biological systems. To appear in Cell Genomics.
- Gamella, J.L., Peters, J. and Bühlmann, P. (2024). The Causal
Chambers as a real-world physical testbed for AI methodology. To appear in
Nature Machine Intelligence. Preprint arXiv:2404.11341
Book
- Bühlmann, P. and van de Geer,
S. (2011).
Statistics for High-Dimensional Data: Methods, Theory and
Applications. Springer.
Edited Books
- Statistical
Analysis for High-Dimensional Data. The Abel Symposium 2014. Edited
by Frigessi, A., Bühlmann, P., Glad, I.K., Langaas, M., Richardson,
S. and Vannucci, M. (2016). Springer.
- Handbook
of Big Data. Edited by Bühlmann, P., Drineas, P., Kane, M. and
van der Laan, M. (2016). Chapman & Hall/CRC.
Book chapters
-
Bühlmann, P. (2001). Time series. Encyclopedia of Environmetrics
(eds. El-Shaarawi, A.H. and Piegorsch, W.W.) , Vol. 4, pp. 2187-2202.
-
Bühlmann, P. (2003). Bagging, subagging and bragging for improving some
prediction algorithms. In Recent Advances and Trends in
Nonparametric Statistics (eds. Akritas, M.G. and Politis, D.N.),
pp. 19-34. Elsevier. PDF
-
Bühlmann, P. (2004). Bagging, boosting and ensemble methods. In Handbook of
Computational Statistics: Concepts and Methods (eds. Gentle, J., Härdle,
W. and Mori, Y.), pp. 877-907. Springer.
-
Hothorn, T., Dettling, M. and Bühlmann, P. (2005). Computational
inference. In Bioinformatics and Computational Biology Solutions using R and
Bioconductor (eds. Gentleman, R., Carey, V., Huber, W., Irizarry, R. and
Dudoit, S.), pp. 293-312. Springer. PDF. See also
here.
-
Bühlmann, P. (2006). Boosting and l^1-penalty methods for
high-dimensional data with some applications in genomics. In From Data and
Information Analysis to Knowledge Engineering (eds. Spiliopoulou, M.,
Kruse, R., Borgelt, C., Nürnberger, A. and Gaul, W.), pp. 1-12. Studies in
Classification, Data Analysis, and Knowledge Organization, Springer.
-
Bühlmann, P. and Lutz, R.W. (2006). Boosting algorithms: with an
application to bootstrapping multivariate time series. In Frontiers in
Statistics (eds. Fan, J. and Koul, H.), pp. 209-230. Imperial College Press.
PDF
-
Schöner D., Barkow S., Bleuler S., Wille A., Zimmermmann P., Bühlmann P.,
Gruissem W. and Zitzler, E. (2007). Network Analysis of Systems
Elements. In Plant Systems Biology, Series: Experientia Supplementum
(eds. Baginsky, S. and Fernie A), pp. 331-351. Birkhäuser.
-
Audrino, F. and Bühlmann, P. (2007). Synchronizing multivariate financial
time series. In The Value-at-Risk Reference (ed. Danielsson,
J.), pp. 261-291. Riskbooks.
-
Bühlmann, P. (2012). Bagging, boosting and ensemble methods. In
Handbook of Computational Statistics: Concepts and
Methods, 2nd edition (eds. Gentle, J., Härdle,
W. and Mori, Y.), pp. 985-1022. Springer. PDF
-
Bühlmann, P. (2013). Comments on Robust Statistics. In Selected Works
of Peter J. Bickel (eds. Fan, J., Ritov, Y. and Wu, C.F.J.),
pp. 51-55. Springer.
-
Bühlmann, P. (2015). Confidence intervals and tests for
high-dimensional models: a compact review. In Modeling and Stochastic
Learning for Forecasting in High Dimensions (eds. Antoniadis, A., Poggi,
J.-M. and Brossat, X.), pp. 21-34. Lecture Notes in Statistics -
Proceedings. Springer. PDF
- Rothenhäusler, D., Meinshausen, N. and Bühlmann,
P. (2016). Confidence intervals for maximin effects in inhomogeneous
large-scale data. In Statistical
Analysis for High-Dimensional Data. The Abel Symposium 2014 (eds. Frigessi, A., Bühlmann, P., Glad, I.K., Langaas, M., Richardson,
S. and Vannucci, M.), pp. 255-277. Springer. PDF
- Bühlmann, P. (2016). Partial least squares for heterogeneous
data. In The Multiple Facets of Partial Least Squares and Related
Methods. PLS, Paris, France, 2014 (eds. Abdi, H., Esposito, V.,
Russolillo, G., Saporta, G. and Trinchera, L.), pp. 3-15. Springer.
-
Bühlmann, P. (2019). Invariance in heterogeneous, large-scale and
high-dimensional data. Proceedings of the International Congress of
Mathematicians (ICM 2018) (eds. Sirakov, B., Ney de Souza, P. and Viana, M.),
pp. 2785-2800. World Scientific. Abstract
Other publication venues
-
Bühlmann, P. and Stuart, A.M. (2016). Mathematics, Statistics and Data
Science. EMS Newsletter, No. 100, June 2016, pages
28-30.PDF
Conference proceedings (not reviewed)
-
Bühlmann, P. (1999). Bootstrapping time series. Bulletin
of the International Statistical Institute, 52nd session. Proceedings, Tome
LVIII, Book1, 201-204.
-
Bühlmann, P. (2003). Boosting methods: why they can be useful for
high-dimensional data. Proceedings of the 3rd International Workshop on
Distributed Computing (DSC 2003).
PDF
-
Bühlmann, P. (2007). Variable selection for high-dimensional data: with
applications in molecular biology. Bulletin
of the International Statistical Institute, 56nd session. PDF
-
Schäfer, J. and Bühlmann, P. (2007). Modeling inhomogeneous
high-dimensional data-sets: with applications in learning large-scale gene
correlations. S.Co. 2007. PDF
Unpublished Papers
-
Bühlmann, P. (1996). Confidence regions for trends in time series:
a Simultaneous Approach with a Sieve Bootstrap. Tech. Rep. 447. UC
Berkeley. Superseded by Bühlmann (1998): Sieve bootstrap for smoothing in
nonstationary time series (see above No. 10).
-
Bühlmann, P. (2002). Consistency for L2Boosting and matching pursuit with
trees and tree-type basis functions. Superseded by Bühlmann (2006):
Boosting for high-dimensional linear models (see above No. 43).
-
Bühlmann, P. and Ferrari, F. (2003). Dynamic combination of models for
nonlinear time series.
PDF
-
Meinshausen, N. and Bühlmann, P. (2003). Discoveries at risk.Compressed
postscript.
PDF
-
Wille, A. and Bühlmann, P. (2004). Tri-Graph: a novel graphical model with
application to genetic regulatory networks. Superseded by Wille and
Bühlmann (2006): Low-order conditional independence
graphs for inferring genetic networks (see above No. 41).
-
Bühlmann, P. and Yu, B. (2005). Boosting, model selection, lasso and
nonnegative garrote. Superseded by Bühlmann and Yu (2006): Sparse Boosting
(see above No. 46).
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Wille, A., Bleuler, S. and Bühlmann, P. (2005). Integrating gene expression
data into flux balance analysis.
- Schöner, D., Dahinden, C., Gruissem, W. and Bühlmann,
P. (2009). Robust prediction of hubs in the yeast synthetic lethal
network.
- Leonardi, F. and Bühlmann, P. (2016). Computationally efficient change point detection for high-dimensional regression. Preprint arXiv:1601.03704
- Li, S. and Bühlmann, P. (2018). Estimating heterogeneous
treatment effects in nonstationary time series with state-space models. Preprint arXiv:1812.04063
- Kovács, S., Ruckstuhl, T., Obrist, H. and Bühlmann,
P. (2021). Graphical Elastic Net and target matrices: Fast algorithms and software for sparse precision matrix estimation. Preprint arXiv:2101.02148
- Azadkia, M., Taeb, A. and Bühlmann, P. (2021). A fast
non-parametric approach for local causal structure learning. Preprint arXiv:2111.14969