Nicolai Meinshausen
Professor of Statistics
Seminar for Statistics, ETH Zurich
Raemistrasse 101, 8092 Zurich
Email: last name @

Research Interests

Computational Statistics, Causality, High-dimensional Data, Machine Learning.
I am currently Associate Editor for the Journal of Machine Learning Research and the Journal of the Royal Statistical Society, Series B.


Most publications and working papers are on Google Scholar.

Causality and Inhomogeneous Data

Jonas Peters, Peter Buehlmann and Nicolai Meinshausen (2016)
Causal inference using invariant prediction: identification and confidence intervals
to appear in Journal of the Royal Statistical Society, Series B (with discussion)
(PDF, abstract at arxiv:stat/1501.01332)

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

Dominik Rothenhaeusler, Christina Heinze, Jonas Peters and Nicolai Meinshausen (2015)
backShift: Learning causal cyclic graphs from unknown shift interventions
Advances in Neural Information Processing Systems 28, 1513--1521
(abstract at arxiv:stat/1506.02494)

Peter Buehlmann and Nicolai Meinshausen (2016)
Magging: maximin aggregation for inhomogeneous large-scale data
Proceedings of the IEEE 104, 126-135
(PDF, abstract at arxiv:stat/1409.2638)

Nicolai Meinshausen and Peter Buehlmann (2015)
Maximin effects in inhomogeneous large-scale data
Annals of Statistics 43 (4), 1801-1830
(PDF, abstract at arxiv:stat/1406.0596)

Dominik Rothenhaeusler, Nicolai Meinshausen and Peter Buehlmann (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
(abstract at arxiv:stat/1502.07963)

High-dimensional Data Analysis

Ruben Dezeure, Peter Buehlmann, Lukas Meier and Nicolai Meinshausen (2015)
High-dimensional Inference: Confidence intervals, p-values and R-Software hdi
Statistical Science 30, 533-558
(PDF, abstract at arxiv:stat/1408.4026),

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, abstract at arxiv:stat/1309.3489)

Nicolai Meinshausen (2013)
Sign-constrained least squares estimation for high-dimensional regression
Electronic Journal of Statistics 7, 1607-1631
(PDF, abstract at arXiv:stat/1202.0889)

Nicolai Meinshausen and Peter Buehlmann (2010)
Stability Selection (with discussion)
Journal of the Royal Statistical Society, Series B 72, 417-473
(PDF, abstract at arXiv:stat/0809.2932)

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, abstract at arxiv:stat/0803.3134)

Nicolai Meinshausen, Lukas Meier and Peter Buehlmann (2009)
P-values for high-dimensional regression
Journal of the American Statistical Association 104, 1671-1681
(PDF, abstract at arXiv:stat/0811.2177)

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, abstract at arXiv:stat/1006.2940)

Nicolai Meinshausen and Bin Yu (2009)
Lasso-type recovery of sparse representations for high-dimensional data
Annals of Statistics 37(1), 246-270
(PDF, abstract at arxiv:stat/0806.0145)

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

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

Nicolai Meinshausen and Peter Buehlmann (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, abstract at arxiv:math/0608017)

Machine Learning

Rajen Shah and Nicolai Meinshausen
Min-wise hashing for large-scale regression and classification with sparse data
(PDF, abstract at arxiv:stat/1308.1269)

Rajen Shah and Nicolai Meinshausen (2014)
Random Intersection Trees
Journal of Machine Learning Research 15, 629-654
(PDF, abstract at arxiv:stat/1303.6223)

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

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

Nicolai Meinshausen (2010)
Node Harvest
Annals of Applied Statistics 4(4), 2049-2072
(PDF, abstract at arXiv:stat/0910.2145, R package)

Nicolai Meinshausen (2009)
Forest Garrote
Electronic Journal of Statistics 3, 1288-1304
(PDF, abstract at arXiv:stat/0906.3590)

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

Applied Work

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

Jorge Zeron-Medina, Xuting Wang, Emmanouela Repapi, Michelle R. Campbell, Dan Su, Francesc Castro-Giner, Benjamin Davies, Elisabeth F.P. Peterse, Natalia Sacilotto, Graeme J. Walker, Tamara Terzian, Ian P. Tomlinson, Neil F. Box, Nicolai Meinshausen, Sarah De Val, Douglas A. Bell, Gareth L. Bond (2013)
A Polymorphic p53 Response Element in KIT Ligand Influences Cancer Risk and Has Undergone Natural Selection
Cell 155(2), 410-422

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

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)

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

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

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
(abstract at arxiv:astro-ph/0611527)

Invited Discussion Contributions

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

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

Nicolai Meinshausen and Peter Buehlmann (2008)
Discussion of: Treelets -- An Adaptive Multi-Scale Basis for Sparse Unordered Data
Annals of Applied Statistics 2(2), 478-481
(PDF, abstract at arxiv:stat/0807.4018)

Multiple Testing

Nicolai Meinshausen and Marloes Maathuis and Peter Buehlmann (2011)
Optimality of the Westfall-Young permutation procedure for multiple testing under dependence
Annals of Statistics 39(6), 3369-3391
(PDF, abstract at arXiv:stat/1106.2068)

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

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

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, abstract at arxiv:math/0501289, software: R-package)

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

Distributed Optimization

Christina Heinze, Brian McWilliams and Nicolai Meinshausen
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
(abstract at arxiv:stat/1506.02554), to appear in AISTATS 2016 (oral)

Brian McWilliams, Christina Heinze, Nicolai Meinshausen, Gabriel Krummenacher and Hastagiri Vanchinathan
LOCO: Distributing Ridge Regression with Random Projections
(abstract at arxiv:stat/1406.3469)

Dynamic Programming and Dual Monte Carlo methods

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, abstract at arxiv:stat/0712.1663)

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