N
Nicolai Meinshausen
Researcher at ETH Zurich
Publications - 131
Citations - 17545
Nicolai Meinshausen is an academic researcher from ETH Zurich. The author has contributed to research in topics: Estimator & Causal model. The author has an hindex of 41, co-authored 127 publications receiving 14196 citations. Previous affiliations of Nicolai Meinshausen include University of Oxford & École Polytechnique Fédérale de Lausanne.
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Journal ArticleDOI
High-dimensional graphs and variable selection with the Lasso
TL;DR: It is shown that neighborhood selection with the Lasso is a computationally attractive alternative to standard covariance selection for sparse high-dimensional graphs and is hence equivalent to variable selection for Gaussian linear models.
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Greenhouse-gas emission targets for limiting global warming to 2 °C
Malte Meinshausen,Nicolai Meinshausen,William Hare,Sarah C. B. Raper,Katja Frieler,Reto Knutti,David J. Frame,Myles R. Allen +7 more
TL;DR: A comprehensive probabilistic analysis aimed at quantifying GHG emission budgets for the 2000–50 period that would limit warming throughout the twenty-first century to below 2 °C, based on a combination of published distributions of climate system properties and observational constraints is provided.
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Warming caused by cumulative carbon emissions towards the trillionth tonne
Myles R. Allen,David J. Frame,Chris Huntingford,Chris D. Jones,Jason Lowe,Malte Meinshausen,Nicolai Meinshausen +6 more
TL;DR: It is found that the peak warming caused by a given cumulative carbon dioxide emission is better constrained than the warming response to a stabilization scenario, and policy targets based on limiting cumulative emissions of carbon dioxide are likely to be more robust to scientific uncertainty than emission-rate or concentration targets.
Journal Article
Quantile Regression Forests
TL;DR: It is shown here that random forests provide information about the full conditional distribution of the response variable, not only about the conditional mean, in order to be competitive in terms of predictive power.
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Lasso-type recovery of sparse representations for high-dimensional data
Nicolai Meinshausen,Bin Yu +1 more
TL;DR: Even though the Lasso cannot recover the correct sparsity pattern, the estimator is still consistent in the ‘2-norm sense for fixed designs under conditions on (a) the number sn of non-zero components of the vector n and (b) the minimal singular values of the design matrices that are induced by selecting of order sn variables.