Peter Bühlmann

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  1. Bühlmann, P. (1994). Blockwise bootstrapped empirical processes for stationary sequences. Annals of Statistics 22, 995-1012.
  2. Bühlmann, P. (1995). The blockwise bootstrap for general empirical processes of stationary sequences. Stochastic Processes and their Applications 58, 247-265.
  3. 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.
  4. Bühlmann, P. (1995). Moving-average representation for autoregressive approximations. Stochastic Processes and their Applications 60, 331-342.
  5. Bühlmann, P. (1996). Locally adaptive lag-window spectral estimation. Journal Time Series Analysis 17, 247-270.
  6. Bickel, P.J. and Bühlmann, P. (1996). What is a linear process? Proceedings National Academy of Sciencies USA 93, 12128-12131.
  7. Bühlmann, P. (1997). Sieve Bootstrap for Time Series. Bernoulli 3, 123-148.
  8. Bickel, P.J. and Bühlmann, P. (1997). Closure of linear processes. Journal of Theoretical Probability 10, 445-479.
  9. Bühlmann, P. (1998). Extreme events from return-volume process: a discretization approach for complexity reduction. Applied Financial Economics 8, 267-278.
  10. Bühlmann, P. (1998). Sieve bootstrap for smoothing in nonstationary time series. Annals of Statistics 26, 48-83.
  11. Bühlmann, P. (1999). Efficient and adaptive post-model-selection estimators. Journal of Statistical Planning and Inference 79, 1-9.
  12. Bühlmann, P. (1999). Dynamic adaptive partitioning for nonlinear time series. Biometrika 86, 555-571. Extended version (compressed postscript).
  13. Bühlmann, P. and Bühlmann, H. (1999). Selection of credibility regression models. ASTIN Bulletin (Journal of the International Actuarial Association) 29, 245-270.
  14. 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.
  15. Bühlmann, P. and Künsch, H.R. (1999). Block length selection in the bootstrap for time series. Computational Statistics & Data Analysis 31, 295-310.
  16. Bühlmann, P. and Wyner, A.J. (1999). Variable length Markov chains. Annals of Statistics 27, 480-513.
  17. 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.
  18. Bühlmann, P. (2000). Von Daten zu stochastischen Modellen (in German). Elemente der Mathematik 55, 1-18. Compressed postscript.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. Bühlmann, P. (2002). Bootstraps for time series. Statistical Science 17, 52-72. Compressed postscript.
  25. 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.
  26. Bühlmann, P. and Yu, B. (2002). Analyzing bagging. Annals of Statistics 30, 927-961. Compressed postscript.
  27. Bühlmann, P. and McNeil, A.J. (2002). An algorithm for nonparametric GARCH modelling. Computational Statistics & Data Analysis 40, 665-683. Compressed postscript.
  28. Dettling, M. and Bühlmann, P. (2002). Supervised clustering of genes.Genome Biology 3(12): research0069.1-0069.15. Software.
  29. 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
  30. Dettling, M. and Bühlmann, P. (2003). Boosting for tumor classification with gene expression data. Bioinformatics 19, No. 9, 1061-1069. Compressed postscript. PDF. Software.
  31. Bühlmann, P. and Yu, B. (2003). Boosting with the L2 loss: regression and classification. Journal of the American Statistical Association 98, 324-339. PDF
  32. Audrino, F. and Bühlmann, P. (2004). Synchronizing multivariate financial time series. The Journal of Risk 6 (2), 81-106. PDF
  33. 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
  34. 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.
  35. Dettling, M. and Bühlmann, P. (2004). Finding predictive gene groups from microarray data. Journal of Multivariate Analysis 90, 106-131. Compressed postscript. PDF
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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
  41. 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.
  42. 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.
  43. Bühlmann, P. (2006). Boosting for high-dimensional linear models. Annals of Statistics 34, 559-583. PDF
  44. Lutz, R.W. and Bühlmann, P. (2006). Boosting for high-multivariate responses in high-dimensional linear regression. Statistica Sinica 16, 471-494. PDF
  45. Lutz, R.W. and Bühlmann, P. (2006). Conjugate direction boosting. Journal of Computational and Graphical Statistics 15, 287-311. PDF
  46. Bühlmann, P. and Yu, B. (2006). Sparse Boosting. Journal of Machine Learning Research 7, 1001-1024. PDF
  47. Hothorn, T., Bühlmann, P., Dudoit, S., Molinaro, A. and van der Laan, M. (2006). Survival ensembles. Biostatistics 7, 355-373.Download paper.
  48. 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.
  49. Hothorn, T. and Bühlmann, P. (2006). Model-based boosting in high dimensions. Bioinformatics 22, 2828-2829. PDF
  50. Goeman, J.J. and Bühlmann, P. (2007). Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 23, 980-987. PDF
  51. 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
  52. 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
  53. 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
  54. Bühlmann, P. (2007). Bootstrap schemes for time series (in Russian). Quantile 3, 37-56.PDF
  55. Meier, L. and Bühlmann, P. (2007). Smoothing L1-penalized estimators for high-dimensional time-course data. Electronic Journal of Statistics 1, 597-615. PDF
  56. 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
  57. Bühlmann, P. and Hothorn, T. (2007). Rejoinder of "Boosting algorithms: regularization, prediction and model fitting". Statistical Science 22, 516-522. PDF
  58. 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.
  59. 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
  60. 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.
  61. 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.
  62. Lutz, R.W., Kalisch, M. and Bühlmann, P. (2008). Robustified L2 boosting. Computational Statistics & Data Analysis 52, 3331-3341. PDF
  63. 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
  64. 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
  65. 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.
  66. 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
  67. 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
  68. Audrino, F. and Bühlmann, P. (2009). Splines for financial volatility. Journal of the Royal Statistical Society: Series B 71, 655-670. PDF
  69. Maathuis, M.H., Kalisch, M. and Bühlmann, P. (2009). Estimating high-dimensional intervention effects from observational data. Annals of Statistics 37, 3133-3164.PDF
  70. Meier, L., van de Geer, S. and Bühlmann, P. (2009). High-dimensional additive modeling. Annals of Statistics 37, 3779-3821. PDF
  71. 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.
  72. Rütimann, P. and Bühlmann, P. (2009). High dimensional sparse covariance estimation via directed acyclic graphs. Electronic Journal of Statistics 3, 1133-1160. PDF
  73. 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
  74. Meinshausen, N., Meier, L. and Bühlmann, P. (2009). p-values for high-dimensional regression. Journal of the American Statistical Association 104, 1671-1681. PDF
  75. Bühlmann, P. and Yu, B. (2010). Boosting. Wiley Interdisciplinary Reviews: Computational Statistics 2, 69-74. PDF
  76. 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.
  77. 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.
  78. Bühlmann, P. and Hothorn, T. (2010). Twin Boosting: improved feature selection and prediction. Statistics and Computing 20, 119-138. PDF
  79. 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)
  80. 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
  81. Dahinden, C., Kalisch, M. and Bühlmann, P. (2010). Decomposition and model selection for large contingency tables. Biometrical Journal 52, 233-252. PDF
  82. 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
  83. Städler, N., Bühlmann, P. and van de Geer, S. (2010). l1-penalization for mixture regression models (with discussion). TEST 19, 209-285. PDF. Rejoinder
  84. Meinshausen, N. and Bühlmann, P. (2010). Stability selection (with discussion). Journal of the Royal Statistical Society: Series B 72, 417-473. PDF
  85. 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
  86. Bühlmann, P. (2010). Remembrance of Leo Breiman. Annals of Applied Statistics 4, 1638-1641. PDF
  87. 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.
  88. 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
  89. 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
  90. 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
  91. 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
  92. 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
  93. Bühlmann, P. and Cai, T. (2011). Introduction to the Lehmann special section. Annals of Statistics 39, 2243.
  94. 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
  95. 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
  96. Stekhoven, D.J. and Bühlmann, P. (2012). MissForest - nonparametric missing value imputation for mixed-type data. Bioinformatics 28, 112-118. PDF
  97. 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
  98. 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
  99. 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
  100. 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
  101. 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
  102. 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
  103. 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
  104. Uhler, C., Raskutti, G., Bühlmann, P. and Yu, B. (2013). Geometry of faithfulness assumption in causal inference. Annals of Statistics 41, 436-463. PDF
  105. van de Geer, S. and Bühlmann, P. (2013). l0-penalized maximum likelihood for sparse directed acyclic graphs. Annals of Statistics 41, 536-567. PDF
  106. Bühlmann, P. (2013). Causal statistical inference in high dimensions. Mathematical Methods of Operations Research 77, 357-370.PDF
  107. Bühlmann, P. (2013). Statistical significance in high-dimensional linear models. Bernoulli 19, 1212-1242. PDF
  108. 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
  109. 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
  110. Hothorn, T., Kneib, T. and Bühlmann, P. (2014). Conditional transformation models. Journal of the Royal Statistical Society: Series B 76, 3-37. PDF
  111. 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
  112. 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
  113. Kalisch, M. and Bühlmann, P. (2014). Causal structure learning and inference: a selective review. Quality Technology & Quantitative Management 11, 3-21. Download
  114. Peters, J. and Bühlmann, P. (2014). Identifiability of Gaussian structural equation models with equal error variances. Biometrika 101, 219-228. PDF
  115. 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
  116. 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
  117. Bühlmann, P. (2014). Invited Discussion of Big Bayes stories and BayesBag. Statistical Science 29, 91-94.PDF
  118. 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
  119. 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
  120. 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
  121. 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
  122. 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
  123. 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
  124. 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
  125. 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
  126. 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
  127. 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
  128. Peters, J. and Bühlmann, P. (2015). Structural intervention distance (SID) for evaluating causal graphs. Neural Computation 27,771-799. PDF
  129. Meinshausen, N. and Bühlmann, P. (2015). Maximin effects in inhomogeneous large-scale data. Annals of Statistics 43, 1801-1830. PDF
  130. Bühlmann, P. and van de Geer, S. (2015). High-dimensional inference in misspecified linear models. Electronic Journal of Statistics 9, 1449-1473. Download
  131. 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
  132. Ernest, J. and Bühlmann, P. (2015). Marginal integration for nonparametric causal inference. Electronic Journal of Statistics 9, 3155-3194. Download
  133. 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.
  134. Bühlmann, P. and Meinshausen, N. (2016). Magging: maximin aggregation for inhomogeneous large-scale data. Proceedings of the IEEE 104, 126-135. PDF
  135. 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
  136. 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
  137. Nowzohour, C. and Bühlmann, P. (2016). Score-based causal learning in additive noise models. Statistics 50, 471-485. PDF
  138. 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
  139. 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
  140. 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
  141. 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
  142. 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
  143. 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
  144. 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
  145. Bühlmann, P. (2017). High-dimensional statistics, with applications to genome-wide association studies. EMS Surveys in Mathematical Sciences 4, 45-75. Preprint PDF
  146. Dezeure, R., Bühlmann, P. and Zhang, C.-H. (2017). High-dimensional simultaneous inference with the bootstrap (with discussion). TEST 26, 685-719. Download
  147. Dezeure, R., Bühlmann, P. and Zhang, C.-H. (2017). Rejoinder on: High-dimensional simultaneous inference with the bootstrap. TEST 26, 751-758. Download
  148. 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
  149. 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
  150. 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
  151. Hothorn, T., Möst, L. and Bühlmann, P. (2018). Most likely transformations. Scandinavian Journal of Statistics 45, 110-134. Download
  152. Bühlmann, P. and van de Geer, S. (2018). Statistics for big data: A perspective. Statistics & Probability Letters 136, 37-41. Download
  153. Rothenhäusler, D., Ernest, J. and Bühlmann, P. (2018). Causal inference in partially linear structural equation models. Annals of Statistics 46, 2904-2938. Download
  154. Pfister, N., Bühlmann, P. and Peters, J. (2019). Invariant causal prediction for sequential data. Journal of the American Statistical Association 114, 1264-1276.Download
  155. 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
  156. Bühlmann, P. (2019). Comments on: Data science, big data and statistics. TEST 28, 330-333. Download
  157. 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
  158. 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
  159. 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
  160. Bühlmann, P. (2020). Invariance, Causality and Robustness (with discussion). Statistical Science 35, 404-426. Download
  161. Bühlmann, P. (2020). Rejoinder: Invariance, Causality and Robustness. Statistical Science 35, 434-436. Download
  162. 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
  163. Bühlmann, P. (2020). Toward causality and improving external validity. Proceedings of the National Academy of Sciences USA 117, 25963-25965. Download
  164. 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
  165. 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
  166. 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
  167. Bühlmann, P. and Ćevid, D. (2020). Deconfounding and causal regularization for stability and external validity. International Statistical Review 88, S114-S134. Download
  168. Schultheiss, C., Renaux, C. and Bühlmann, P. (2021). Multicarving for high-dimensional post-selection inference. Electronic Journal of Statistics 15, 1695-1742. Download
  169. 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
  170. Bühlmann, P. (2021). One modern culture of statistics. Comments on Statistical Modeling: The Two Cultures (Breiman, 2001b). Observational Studies 7, 33-40. Download
  171. 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
  172. 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
  173. Chen, Y. and Bühlmann, P. (2021). Domain adaptation under structural causal models. Journal of Machine Learning Research 22, (261): 1-80. Download
  174. Emmenegger, C. and Bühlmann, P. (2021). Regularizing double machine learning in partially linear endogenous models. Electronic Journal of Statistics 15, 6461-6543. Download
  175. 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
  176. 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
  177. 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
  178. Kook, L., Sick, B. and Bühlmann, P. (2022). Distributional anchor regression. Statistics and Computing 32: 39, 1-19. Download
  179. 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
  180. Guo, Z., Ćevid, D. and Bühlmann, P. (2022). Doubly Debiased Lasso: high-dimensional inference under hidden confounding. Annals of Statistics 50, 1320-1347. Download
  181. Scheidegger, C., Hörrmann, J. and Bühlmann, P. (2022). The weighted generalised covariance measure. Journal of Machine Learning Research 23, (273): 1-68. Download
  182. 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
  183. 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
  184. Danielli, S.G., Porpiglia, E., De Micheli, A.J., Navarro, N., Zellinger, M.J., Bechtold, I., Kisele, S., Volken, L., Ngo, Q.A., 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
  185. 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
  186. Shah, R.D. and Bühlmann, P. (2023). Double-estimation-friendly inference for high-dimensional misspecified models. Statistical Science 38, 68-91. Download
  187. 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
  188. 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
  189. 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
  190. Londschien, M., Bühlmann, P. and Kovács, S. (2023). Random Forests for change point detection. Journal of Machine Learning Research 24, (216): 1-45. Download
  191. Rothenhäusler, D. and Bühlmann, P. (2023). Distributionally robust and generalizable inference. Statistical Science, 38, No. 4, 527-542. Download
  192. 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
  193. 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
  194. Schultheiss, C. and Bühlmann, P. (2023). Ancestor regression in linear structural equation models. Biometrika 110, 1117-1124. Download
  195. 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
  196. 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
  197. 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
  198. 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
  199. 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
  200. Taeb, A., Gamella, J.L., Heinze-Deml, C. and Bühlmann, P. (2024). Learning and scoring Gaussian latent variable causal models with unknown additive interventions. Journal of Machine Learning Research 25 (293): 1-68. Download
  201. Kovács, S., Li, H., Haubner, L., Munk, A. and Bühlmann, P. (2024). Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries. Journal of Machine Learning Research 25 (297): 1-64. Download
  202. Qian, L., Sun, R., Aebersold, R., Bühlmann, P., Sander, C. and Guo, T. (2024). AI-empowered perturbation proteomics for complex biological systems. Cell Genomics 4 (11) 100691, 1-16. Download
  203. Gamella, J.L., Peters, J. and Bühlmann, P. (2025). Causal Chambers as a real-world physical testbed for AI methodology. Nature Machine Intelligence 7, 107-118. Download
  204. Scheidegger, C., Guo, Z. and Bühlmann, P. (2025). Spectral deconfounding for high-dimensional sparse additive models. ACM/IMS Journal of Data Science 2 (1), 1-52. Download
  205. Plecko, D., Bennett, N., Ukor, I.-F., Rodemund, N., Serpa-Neto, A. and Bühlmann, P. (2025). A framework and analytical exploration for a data-driven update of the Sequential Organ Failure Assessment (SOFA) score in sepsis. Critical Care and Resuscitation 27 (1) 100105, 1-7. Download
  206. Henzi, A., Shen, X., Law, M. and Bühlmann, P. (2025). Invariant probabilistic prediction. Biometrika 112 (1), asae063 https://doi.org/10.1093/biomet/asae063, 1-22. Download
  207. Zellinger, M.J. and Bühlmann, P. (2025). Natural language-based synthetic data generation for cluster analysis. Journal of Classification, https://doi.org/10.1007/s00357-025-09501-w, 1-27. Download
  208. Emmenegger, C., Spohn, M.-L., Elmer, T. and Bühlmann, P. (2025). Treatment effect estimation with observational network data using machine learning. Journal of Causal Inference 13 (1) 20230082, 1-36. Download
  209. Schultheiss, C., Ulmer, M. and Bühlmann, P. (2025). Ancestor regression in structural vector autoregressive models. Journal of Causal Inference 13 (1) 20240011, 1-25. Download
  210. Carl, D., Emmenegger, C., Bühlmann, P. and Guo, Z. (2025). TSCI: two stage curvature identification for causal inference with invalid instruments in R. Journal of Statistical Software 114, doi: 10.18637/jss.v114.i07, 1-21. Download
  211. Gu, Y., Fang, C., Bühlmann, P. and Fan, J. (2024). Causality pursuit from heterogeneous environments via neural adversarial invariance learning. To appear in the Annals of Statistics. Preprint arXiv:2405.04715
  212. Shen, X., Bühlmann, P. and Taeb, A. (2025). Causality-oriented robustness: exploiting general noise interventions in linear structural causal models. To appear in the Journal of the American Statistical Association. Early access. Preprint arXiv:2307.10299
  213. Wang, Z., Bühlmann, P. and Guo, Z (2023). Distributionally robust learning for muti-souce unsupervised domain adaptation. To appear in the Annals of Statistics. Preprint arXiv:2309.02211 Chosen as invited poster presentation at NeurIPS 2025.
  214. Ulmer, M., Scheidegger, C. and Bühlmann, P. (2025). Spectrally deconfounded Random Forests. To appear in the Journal of Computational and Graphical Statistics. Preprint arXiv:2502.03969

    Book

  215. Bühlmann, P. and van de Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer.

    Edited Books

  216. 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.
  217. Handbook of Big Data. Edited by Bühlmann, P., Drineas, P., Kane, M. and van der Laan, M. (2016). Chapman & Hall/CRC.

    Book chapters

  218. Bühlmann, P. (2001). Time series. Encyclopedia of Environmetrics (eds. El-Shaarawi, A.H. and Piegorsch, W.W.) , Vol. 4, pp. 2187-2202.
  219. 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
  220. 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.
  221. 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.
  222. 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.
  223. 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
  224. 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.
  225. Audrino, F. and Bühlmann, P. (2007). Synchronizing multivariate financial time series. In The Value-at-Risk Reference (ed. Danielsson, J.), pp. 261-291. Riskbooks.
  226. 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
  227. 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.
  228. 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
  229. 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
  230. 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.
  231. 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

  232. 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)

  233. Bühlmann, P. (1999). Bootstrapping time series. Bulletin of the International Statistical Institute, 52nd session. Proceedings, Tome LVIII, Book1, 201-204.
  234. 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
  235. Bühlmann, P. (2007). Variable selection for high-dimensional data: with applications in molecular biology. Bulletin of the International Statistical Institute, 56nd session. PDF
  236. 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

  237. 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).
  238. 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).
  239. Bühlmann, P. and Ferrari, F. (2003). Dynamic combination of models for nonlinear time series. PDF
  240. Meinshausen, N. and Bühlmann, P. (2003). Discoveries at risk.Compressed postscript. PDF
  241. 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).
  242. 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).
  243. Wille, A., Bleuler, S. and Bühlmann, P. (2005). Integrating gene expression data into flux balance analysis.
  244. Schöner, D., Dahinden, C., Gruissem, W. and Bühlmann, P. (2009). Robust prediction of hubs in the yeast synthetic lethal network.
  245. Leonardi, F. and Bühlmann, P. (2016). Computationally efficient change point detection for high-dimensional regression. Preprint arXiv:1601.03704
  246. Li, S. and Bühlmann, P. (2018). Estimating heterogeneous treatment effects in nonstationary time series with state-space models. Preprint arXiv:1812.04063
  247. 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
  248. Azadkia, M., Taeb, A. and Bühlmann, P. (2021). A fast non-parametric approach for local causal structure learning. Preprint arXiv:2111.14969