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Publications Influence

Stability Selection

- N. Meinshausen, P. Bühlmann
- Mathematics
- 17 September 2008

Estimation of structure, such as in variable selection, graphical modelling or cluster analysis, is notoriously difficult, especially for high dimensional data. We introduce stability selection. It… Expand

1,545 211- PDF

Quantile Regression Forests

- N. Meinshausen
- Mathematics, Computer Science
- J. Mach. Learn. Res.
- 1 December 2006

TLDR

783 118- PDF

Greenhouse-gas emission targets for limiting global warming to 2 °C

- M. Meinshausen, N. Meinshausen, +5 authors M. Allen
- Environmental Science, Medicine
- Nature
- 30 April 2009

More than 100 countries have adopted a global warming limit of 2 °C or below (relative to pre-industrial levels) as a guiding principle for mitigation efforts to reduce climate change risks, impacts… Expand

1,915 96- PDF

Warming caused by cumulative carbon emissions towards the trillionth tonne

- M. Allen, D. Frame, +4 authors N. Meinshausen
- Medicine
- Nature
- 30 April 2009

Global efforts to mitigate climate change are guided by projections of future temperatures. But the eventual equilibrium global mean temperature associated with a given stabilization level of… Expand

1,130 81- PDF

LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA

- N. Meinshausen, B. Yu
- Mathematics
- 1 June 2008

The Lasso [28] is an attractive technique for regularization and variable selection for high-dimensional data, where the number of predictor variables p is potentially much larger than the number of… Expand

735 63- PDF

Relaxed Lasso

- N. Meinshausen
- Computer Science
- Comput. Stat. Data Anal.
- 2007

TLDR

275 43- PDF

Causal inference using invariant prediction: identification and confidence intervals

- J. Peters, Peter Buhlmann, N. Meinshausen
- Mathematics, Computer Science
- 6 January 2015

TLDR

249 42- PDF

p-Values for High-Dimensional Regression

- N. Meinshausen, Lukas Meier, Peter Bühlmann
- Mathematics
- 13 November 2008

Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid… Expand

299 41- PDF

Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses

- N. Meinshausen, J. Rice
- Mathematics
- 19 January 2005

We consider the problem of estimating the number of false null hypotheses among a very large number of independently tested hypotheses, focusing on the situation in which the proportion of false null… Expand

187 25- PDF

High-Dimensional Inference: Confidence Intervals, $p$-Values and R-Software hdi

- Ruben Dezeure, Peter Buhlmann, Lukas Meier, N. Meinshausen
- Mathematics
- 18 August 2014

We present a (selective) review of recent frequentist highdimensional
inference methods for constructing p-values and confidence
intervals in linear and generalized linear models. We include a… Expand

129 20- PDF