I am University Lecturer at the Department of Statistics at the University of Oxford and Fellow of Somerville College.
Previously, I worked at the Department of Statistics at ETH Zurich and the University of California, Berkeley.

My research interests include:
Computational Statistics, High-dimensional Data, Regularization, Lasso-type Estimators, Sparsity, Machine Learning, Multiple Testing, Statistics for Astronomy and Climate Science.

Contact Information

Department of Statistics, University of Oxford
1 South Parks Road; Oxford OX1 3TG, UK
Email is my family name `at' stats.ox.ac.uk.
Phone: 01865 - 272593 and 01865- 270586 in Somerville

Teaching

Applied Statistics BS1b
Statistical Data Mining

Professional activities

Associate Editor for
Journal of Computational and Graphical Statistics (08-)
Biometrika (09-)
Journal of the Royal Statistical Society, Series B (09-).

previously for
Electronic Journal of Statistics (07-10)

CV

Recent Invited Talks

Trees, Forests and Node Harvest
Department of Statistics, University of Bath 30/10/09

Trees, Forests and Group Aggregation
Department of Statistics, ETH Zurich 11/9/09

Structure estimation, subsampling and stability
Department of Statistics, University of Edinburgh 1/5/09

Structure estimation, subsampling and stability
Department of Statistics, University of Glasgow 29/4/09

Stability Selection
Workshop on `Sparse Recovery Problems in High Dimensions: Statistical Inference and Learning Theory', Oberwolfach 15/3/09

Stability Selection
Conference on Statistical Regularization and Qualitative Constraints, Goettingen 23/11/08

Applications and theory of sparse signal recovery for high-dimensional data
Seminar for YES-II, Eurandom, Eindhoven 6-8/10/08

Stability and sparsity
7th IMS/Bernoulli World Congress in Probability and Statistics, Singapore 15/7/08

Stability-based regularization
in the programme `Statistical Theory and Methods for Complex, High-Dimensional Data',
Newton Institute, Cambridge 23/6/08

Variable selection, high-dimensional data and stability
Department of Statistics, University of Bristol 23/5/08

Efficient blind search: detecting pulsars under computational cost constraints
Department of Statistics, University of Oxford 8/11/07

Variable selection with the Lasso for high-dimensional data
Meeting of the French Statistical Society, Angers 14/6/07

Some consistency results for Lasso-type variable selection
Department of Statistics, Stanford University 16/1/07

Lasso-type recovery of sparse representations from high-dimensional data
Department of Statistics, Wharton School, University of Pennsylvania 12/12/06

Lasso-type recovery of sparse representations from high-dimensional data
Department of Statistics, UC Berkeley 5/12/06

Lasso-type recovery of sparse representations from high-dimensional data
Workshop on `Qualitative assumptions and regularization for high-dimensional data',
Oberwolfach 6/11/06

Prediction and Model Selection with the Lasso
Department of Statistics, UC Berkeley, 28/10/06

Detection of small objects in the outer solar system. How many are there?
Department of Statistics, UC Davis, 1/6/06

Most publications and working papers are on Google Scholar.
Some papers (all recent) on arXiv.

Working Papers

Nicolai Meinshausen
Node Harvest: simple and interpretable regression and classification
(PDF, abstract at arXiv:stat/0910.2145, R package)

Nicolai Meinshausen and Peter Buehlmann
Stability Selection
to appear in
Journal of the Royal Statistical Society, Series B (with discussion)
(PDF, abstract at arXiv:stat/0809.2932)

Published Papers

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)

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

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

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, abstract at arxiv:stat/0712.1663)

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 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)

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

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

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

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

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

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)

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