Nicolai Meinshausen

Sebastian Sippel, Elizabeth C. Kent, Nicolai Meinshausen, Duo Chan, Christopher Kadow, Raphael Neukom, Erich M. Fischer, Vincent Humphrey, Robert Rohde, Iris de Vries, Reto Knutti (2024)
Early-twentieth-century cold bias in ocean surface temperature observations
Nature 635, 618–624
(News & Views)

Xinwei Shen, Nicolai Meinshausen (2024)
Engression: Extrapolation through the Lens of Distributional Regression
to appear in Journal of the Royal Statistical Society, Series B
arxiv:2307.00835
R and python implementation

Myles R. Allen, David J. Frame, Pierre Friedlingstein, Nathan P. Gillett, Giacomo Grassi, Jonathan M. Gregory, William Hare, Jo House, Chris Huntingford, Stuart Jenkins, Chris D. Jones, Reto Knutti, Jason A. Lowe, H. Damon Matthews, Malte Meinshausen, Nicolai Meinshausen, Glen P. Peters, Gian-Kasper Plattner, Sarah Raper, Joeri Rogelj, Peter A. Stott, Susan Solomon, Thomas F. Stocker, Andrew J. Weaver, Kirsten Zickfeld (2024)
Geological Net Zero and the need for disaggregated accounting for carbon sinks
Nature

Jeffrey Näf, Meta-Lina Spohn, Loris Michel, Nicolai Meinshausen (2024)
PKLM: a flexible MCAR test using classification
to appear in Psychometrika

Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann, Nicolai Meinshausen (2023)
Confidence and Uncertainty Assessment for Distributional Random Forests
Journal of Machine Learning Research 24(366):1−77

Xinwei Shen, Nicolai Meinshausen (2024)
Distributional Principal Autoencoders
arxiv:2404.13649

Michael Moor, Nicolas Bennet, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten Borgwardt (2023)
Predicting sepsis using deep learning across international sites: a restrospective development and validation study
EClinical Medicine 62
arxiv:2107.05230

Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit (2022)
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) arxiv:2204.10022

Domagoj Ćevid, Loris Michel, Jeffrey Näf, Nicolai Meinshausen, Peter Bühlmann (2022)
Distributional random forests: Heterogeneity adjustment and multivariate distributional regression
Journal of Machine Learning Research 23, 1-79
arxiv:2005.14458

Sebastian Sippel, Nicolai Meinshausen, Eniko Szekely, Erich Fischer, Angeline Pendergrass, Flavio Lehner, Reto Knutti (2021)
Robust detection of forced warming in the presence of potentially large climate variability
Science Advances 7(43)

Jonas Peters and Nicolai Meinshausen (2021)
The Raven's Hat Fallen Pictures, Rising Sequences, and Other Mathematical Games
MIT press

Jeffrey Näf, Meta-Lina Spohn, Loris Michel, Nicolai Meinshausen (2023)
Imputation Scores
The Annals of Applied Statistics 17 (3), 2452-2472 arxiv 2106.03742

Christina Heinze-Deml, Sebastian Sippel, Angeline G Pendergrass, Flavio Lehner, Nicolai Meinshausen (2021),
Latent Linear Adjustment Autoencoder v1. 0: a novel method for estimating and emulating dynamic precipitation at high resolution
Geoscientific Model Development 14(8), 4977-4999

Nicola Gnecco, Nicolai Meinshausen, Jonas Peters, Sebastian Engelke (2021) ,
Causal discovery in heavy-tailed models
The Annals of Statistics 3, 1755-1778

Sebastian Sippel, Erich M Fischer, Simon C Scherrer, Nicolai Meinshausen, Reto Knutti (2020),
Late 1980s abrupt cold season temperature change in Europe consistent with circulation variability and long-term warming
Environmental Research Letters 15(9)

Malte Meinshausen, Zebedee RJ Nicholls, Jared Lewis, Matthew J Gidden, Elisabeth Vogel, Mandy Freund, Urs Beyerle, Claudia Gessner, Alexander Nauels, Nico Bauer, Josep G Canadell, John S Daniel, Andrew John, Paul B Krummel, Gunnar Luderer, Nicolai Meinshausen, Stephen A Montzka, Peter J Rayner, Stefan Reimann, Steven J Smith, Marten van den Berg, Guus JM Velders, Martin K Vollmer, Ray HJ Wang (2020) ,
The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500
Geoscientific Model Development 13(8), 3571-3605

Mischa Külling, Roberto Corti, Georg Noll, Silke Küest, David Hürlimann, Christophe Wyss, Ivano Reho, Felix C Tanner, Jeremy Külling, Nicolai Meinshausen, Oliver Gaemperli, Peter Wenaweser, Sacha P Salzberg, Thierry Aymard, Jürg Grünenfelder, Patric Biaggi (2020),
Heart team approach in treatment of mitral regurgitation: patient selection and outcome
Open Heart 7(2)

Sebastian Sippel, Nicolai Meinshausen, Erich M Fischer, Enikő Székely, Reto Knutti (2020),
Climate change now detectable from any single day of weather at global scale
Nature Climate Change !0(1), 35-41
(arxiv)

Drago Plečko, Nicolai Meinshausen (2019),
Fair Data Adaptation with Quantile Preservation
Journal of Machine Learning Research 21, 1-44
(arxiv)

Eniko Székely, Sebastian Sippel, Reto Knutti, Guillaume Obozinski, Nicolai Meinshausen (2019),
A direct approach to detection and attribution of climate change
(arxiv)

Sebastian Sippel, Nicolai Meinshausen, Anna Merrifield, Flavio Lehner, Angeline G Pendergrass, Erich Fischer, Reto Knutti (2019),
Uncovering the forced climate response from a single ensemble member using statistical learning
Journal of Climate 32(17), 5677-5699
(arxiv)

Rajen Shah and Nicolai Meinshausen (2020),
Right singular vector projection graphs: fast High-dimensional Covariance Matrix Estimation under Latent Confounding
Journal of the Royal Statistical Society, Series B, 82(2), 361-389
(arxiv)

Domagoj Cevid, Peter Bühlmann, Nicolai Meinshausen (2020),
Spectral Deconfounding and Perturbed Sparse Linear Models
Journal of Machine Learning Research , 21, 1-41
(arxiv)

Dominik Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann, Jonas Peters (2021),
Anchor regression: heterogeneous data meets causality
Journal of the Royal Statistical Society, Series B, 83(2), 215-246
(arxiv)

Christina Heinze-Deml, Jonas Peters and Nicolai Meinshausen (2018),
Invariant Causal Prediction for Nonlinear Models
Journal of Causal Inference , 6(2)
(arxiv),

Christina Heinze-Deml and Nicolai Meinshausen (2021),
Conditional Variance Penalties and Domain Shift Robustness
Machine Learning 110, 303-348
(arxiv)

Dominik Rothenhaeusler, Peter Bühlmann, Nicolai Meinshausen (2019)
Causal Dantzig: fast inference in linear structural equation models with hidden variables under additive interventions
Annals of Statistics, 47 (3), 1688-1722
(arxiv)

Luca Weihs, Mathias Drton and Nicolai Meinshausen (2018)
Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence
Biometrika, 105 (3), 547-562
(arxiv)

Gian Thanei, Nicolai Meinshausen and Rajen Shah (2018)
The xyz algorithm for fast interaction search in high-dimensional data
Journal of Machine Learning Research, 9 (37), 1−42
(arxiv)

Nicolai Meinshausen (2018)
Causality from a distributional robustness point of view
2018 IEEE Data Science Workshop, 6-10

Christina Heinze-Deml, Marloes Maathuis, and Nicolai Meinshausen (2018)
Causal Structure Learning
Annual Review of Statistics and its Applications, 5, 371-391
(arxiv)

Rajen Shah and Nicolai Meinshausen (2018)
On b-bit min-wise hashing for large-scale regression and classification with sparse data
Journal of Machine Learning Research, 18 (178), 1−42
(PDF, arxiv)

Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen (2018)
Preserving Differential Privacy Between Features in Distributed Estimation
Stat, 7, e189
(arxiv)

Malte Meinshausen, Elisabeth Vogel, Alexander Nauels, Katja Lorbacher, Nicolai Meinshausen, David M. Etheridge, Paul J. Fraser, Stephen A. Montzka, Peter J. Rayner Cathy M. Trudinger, Paul B. Krummel, Urs Beyerle, Josep G. Canadell, John S. Daniel, Ian G. Enting, Rachel M. Law, Chris R. Lunder, Simon O'Doherty, Ron G. Prinn, Stefan Reimann, Mauro Rubino, Guus J. M. Velders, Martin K. Vollmer, Ray H. J. Wang, and Ray Weiss (2017)
Historical greenhouse gas concentrations for climate modelling (CMIP6)
Geoscientific Model Development, 10, 2057-2116

Gian-Andrea Thanei, Christina Heinze, Nicolai Meinshausen (2017)
Random Projections for Large-Scale Regression
Big and Complex Data Analysis, Springer, 51-68

Jonas Peters, Peter Bühlmann and Nicolai Meinshausen (2016)
Causal inference using invariant prediction: identification and confidence intervals
Journal of the Royal Statistical Society, Series B (with discussion), 78, 947-1012
(PDF, arxiv)

Christina Heinze, Brian McWilliams and Nicolai Meinshausen (2016)
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
AISTATS 2016 (PMLR) , 875-883

Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann and Nicolai Meinshausen (2016)
Scalable Adaptive Stochastic Optimization Using Random Projections
NIPS 2016, 1750-1758

Nicolai Meinshausena, Alain Hauser, Joris Mooij, Jonas Peters, Philip Versteeg and Peter Bühlmann (2016),
Methods for causal inference from gene perturbation experiments and validation
Proceedings of the National Academy of Sciences, 113 (27), 7361-7368

Peter Bühlmann and Nicolai Meinshausen (2016)
Magging: maximin aggregation for inhomogeneous large-scale data
Proceedings of the IEEE 104, 126-135
(PDF, arxiv)

Dominik Rothenhaeusler, Nicolai Meinshausen and Peter Bühlmann (2016)
Confidence Intervals for Maximin Effects in Inhomogeneous Large-Scale Data
in Statistical Analysis for High-Dimensional Data, The Abel Symposium 2014, Springer, 255-277
(arxiv)

Nicolai Meinshausen and Peter Bühlmann (2015)
Maximin effects in inhomogeneous large-scale data
Annals of Statistics 43 (4), 1801-1830
(PDF, arxiv)

Ruben Dezeure, Peter Bühlmann, Lukas Meier and Nicolai Meinshausen (2015)
High-dimensional Inference: Confidence intervals, p-values and R-Software hdi
Statistical Science 30, 533-558
(PDF, arxiv)

Dominik Rothenhaeusler, Christina Heinze, Jonas Peters and Nicolai Meinshausen (2015)
backShift: Learning causal cyclic graphs from unknown shift interventions
NIPS 2015, 1513-1521
(arxiv)

Aurelie Lozano, Nicolai Meinshausen, Eunho Yang (2016)
Minimum Distance Lasso for robust high-dimensional regression
Electronic Journal of Statistics 10 (1), 1296-1340

Nicolai Meinshausen (2015)
Group-bound: confidence intervals for groups of variables in sparse high-dimensional regression without assumptions on the design
Journal of the Royal Statistical Society, Series B 77 (5), 923-945
(PDF, arxiv)

Jonas R. Klasen, Elke Barbez, Lukas Meier, Nicolai Meinshausen, Peter Bühlmann, Maarten Koornneef, Wolfgang Busch and Korbinian Schneeberger (2016)
A multi-marker association method for genome-wide association studies without the need for population structure correction
Nature Communications, 7

Malte Meinshausen, Louise Jeffery, Johannes Guetschow, Yann Robiou du Pont, Joeri Rogelj, Michiel Schaeffer, Niklas Hoehne, Michel den Elzen, Sebastian Oberthuer,and Nicolai Meinshausen (2015)
National post-2020 greenhouse gas targets and diversity-aware leadership
Nature Climate Change 5,1098–1106

Tze Choy and Nicolai Meinshausen (2014)
Sparse distance metric learning
Computational Statistics 29, 515-528

Rajen Shah and Nicolai Meinshausen (2014)
Random Intersection Trees
Journal of Machine Learning Research 15, 629-654
(PDF, arxiv)

Nicolai Meinshausen (2010)
Node Harvest
Annals of Applied Statistics 4 (4), 2049-2072
(PDF, arXiv, R package)

Nicolai Meinshausen (2013)
Sign-constrained least squares estimation for high-dimensional regression
Electronic Journal of Statistics 7, 1607-1631
(PDF, arxiv)

Nicolai Meinshausen (2013)
Discussion of "Grouping Strategies and Thresholding for High Dimension Linear Models"
Journal of Statistical Planning and Inference 143 (9), 1439-1440

Zhou Fang and Nicolai Meinshausen (2012)
Liso Isotone for High-Dimensional Additive Isotonic Regression
Journal of Computational and Graphical Statistics 21 (1), 72-91
(PDF, arxiv)

Daniel Rowlands, David Frame, Duncan Ackerley, Tolu Aina, Ben Booth, Carl Christensen, Matthew Collins, Nicholas Faull, Chris Forest, Benjamin Grandey, Edward Gryspeerdt, Eleanor Highwood, William Ingram, Sylvia Knight, Ana Lopez, Neil Massey, Frances McNamara, Nicolai Meinshausen, Claudio Piani, Suzanne Rosier, Benjamin Sanderson, Leonard Smith, Daith Stone, Milo Thurston, Kuniko Yamazaki, Hiro Yamazaki and Myles Allen (2012)
Broad range of 2050 warming from an observationally constrained large climate model ensemble
Nature Geoscience 5, 256-260

Nicolai Meinshausen (2011)
Partition Maps
Journal of Computational and Graphical Statistics 20 (4), 1007-1028
(PDF, R package)

Steffen Lauritzen and Nicolai Meinshausen (2012)
Discussion: Latent Variable Graphical Model Selection Via Convex Optimization
Annals of Statistics 40 (4), 1973-1977

Nicolai Meinshausen (2011)
Discussion of "Multiple Testing for Exploratory Research"
Statistical Science 26 (4), 601-603
(PDF)

Nicolai Meinshausen and Marloes Maathuis and Peter Bühlmann (2011)
Optimality of the Westfall-Young permutation procedure for multiple testing under dependence
Annals of Statistics 39 (6), 3369-3391
(PDF, arxiv)

Nicolai Meinshausen (2009)
Forest Garrote
Electronic Journal of Statistics 3, 1288-1304
(PDF, arxiv)

Nicolai Meinshausen, Lukas Meier and Peter Bühlmann (2009)
P-values for high-dimensional regression
Journal of the American Statistical Association 104, 1671-1681
(PDF, arXiv)

Nicolai Meinshausen and Peter Bühlmann (2010)
Stability Selection (with discussion)
Journal of the Royal Statistical Society, Series B 72, 417-473
(PDF, arxiv)

Nicolai Meinshausen and Bin Yu (2009)
Lasso-type recovery of sparse representations for high-dimensional data
Annals of Statistics 37 (1), 246-270
(PDF, arxiv)

Myles Allen, David Frame, Chris Huntingford, Chris Jones, Jason Lowe, Malte Meinshausen and Nicolai Meinshausen (2009)
Warming caused by cumulative carbon emissions towards the trillionth tonne
Nature 458, 1163-1166

Nicolai Meinshausen and Peter Bühlmann (2008)
Discussion of: Treelets -- An Adaptive Multi-Scale Basis for Sparse Unordered Data
Annals of Applied Statistics 2 (2), 478-481
(PDF, arxiv)

Malte Meinshausen, Nicolai Meinshausen, William Hare, Sarah Raper, Katja Frieler, Reto Knutti, David Frame and Myles Allen (2009)
Greenhouse-gas emission targets for limiting global warming to 2 C
Nature 458, 1158-1162
(Nature Editorial, News & Views)

Nicolai Meinshausen, Peter Bickel and John Rice (2009)
Efficient Blind Search: Optimal Power of Detection under Computational Cost Constraints
Annals of Applied Statistics 3 (1), 38-60
(PDF, arxiv)

M. Allen, D. Frame, K. Frieler, W. Hare, C. Huntingford, C. Jones, R. Knutti, J. Lowe, M. Meinshausen, N. Meinshausen, and S. Raper (2009)
The exit strategy
Nature Reports Climate Change, 56-58

Nicolai Meinshausen (2008)
A Note on the Lasso for Graphical Gaussian Model Selection
Statistics & Probability Letters 78 (7), 880-884
(PDF)

Nicolai Meinshausen (2007)
Relaxed Lasso
Computational Statistics and Data Analysis 52 (1), 374-393
(PDF, R-package)

Nicolai Meinshausen (2006)
Quantile Regression Forests
Journal of Machine Learning Research 7, 983-999
(PDF, R-package)

Weng-Ping Chen, Charles Alcock, Tim Axelrod, Federica Bianco, Yong-Ik Byun, Hsiang-Kuang Chang, Kem Cook, Rahul Dave, Joseph Giammarco, D. Kim, Sun-Kung King, Typhoon Lee, Matthew Lehner, Chun-Che Lin, Lupin Lin, Jack Lissauer, Stuart Marshall, Nicolai Meinshausen, Soumen Mondal, Imke De Pater, Rodin Porrata, John Rice, Megan Schwamb, Andrew Wang, Shiang-Yu Wang, Chih-Yi Wen and Zhi-Wei Zhang (2006)
Search for small trans-Neptunian objects by the TAOS project
Proceedings of the International Astronomical Union 2, 65-68
Cambridge University Press
(arxiv)

Nicolai Meinshausen and Peter Bühlmann (2006)
High dimensional graphs and variable selection with the Lasso
Annals of Statistics 34 (3), 1436-1462
An interview in Essential Science Indicators in January 2008.
(PDF, arxiv)

Nicolai Meinshausen, Guilherme Rocha and Bin Yu (2007)
Discussion: A tale of three cousins: Lasso, L2Boosting and Dantzig
Annals of Statistics 35 (6), 2373-2384
(PDF, arxiv)

Nicolai Meinshausen (2006)
False discovery control for multiple tests of association under general dependence
Scandinavian Journal of Statistics 33 (2), 227-237
(PDF)

Nicolai Meinshausen (2008)
Hierarchical testing of variable importance
Biometrika 95 (2), 265-278
(PDF)

Nicolai Meinshausen and John Rice (2006)
Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses
Annals of Statistics 34 (1), 373-393
(PDF, arxiv, R-package)

Nicolai Meinshausen and Peter Bühlmann (2005)
Lower bounds for the number of false null hypotheses for multiple testing of associations
Biometrika 92 (4), 893-907
(PDF, R-package)

Nicolai Meinshausen and Ben Hambly (2004)
Monte carlo methods for the valuation of multiple exercise options
Mathematical Finance 14 (4) 557-583
(PDF)