Nicolai Meinshausen

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Peter BühlmannProfessor of Statistics and Mathematics, ETH ZurichEmail verificata su stat.math.ethz.ch
Malte MeinshausenProf, University of MelbourneEmail verificata su unimelb.edu.au
Reto KnuttiProfessor for Climate Physics, ETH ZurichEmail verificata su env.ethz.ch
Jonas PetersProfessor of Statistics, ETH ZurichEmail verificata su stat.math.ethz.ch
Christina Heinze-DemlApple AI/ML ResearchEmail verificata su apple.com
Elisabeth VogelWater Research Centre, UNSWEmail verificata su unsw.edu.au
Erich FischerETH ZurichEmail verificata su env.ethz.ch
Sebastian SippelLeipzig UniversityEmail verificata su uni-leipzig.de
Dominik RothenhäuslerStanford UniversityEmail verificata su stanford.edu
Bin YUProfessor of Statistics and EECS, UC BerkeleyEmail verificata su stat.berkeley.edu
Lukas MeierETH ZürichEmail verificata su stat.math.ethz.ch
Rajen D. ShahUniversity of CambridgeEmail verificata su cam.ac.uk
Brian McWilliamsGoogle DeepMindEmail verificata su google.com
Angeline G PendergrassCornell University and National Center for Atmospheric ResearchEmail verificata su ucar.edu
Ben HamblyMathematical Institute, University of OxfordEmail verificata su maths.ox.ac.uk
Joris M. MooijProfessor in Mathematical Statistics, Korteweg-de Vries Institute, University of Amsterdam (NL)Email verificata su uva.nl
Xinwei ShenETH ZurichEmail verificata su stat.math.ethz.ch
Gareth BondUniversity of OxfordEmail verificata su ndm.ox.ac.uk
Mathias DrtonProfessor of Mathematical Statistics, Technical University of MunichEmail verificata su tum.de
Luca Weihsprev. Research Scientist, Allen Institute for Artificial IntelligenceEmail verificata su allenai.org

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Nicolai Meinshausen

Professor of Statistics, ETH Zurich

Email verificata su stat.math.ethz.ch

Causality Machine Learning High-Dimensional Data


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
High-dimensional graphs and variable selection with the lasso N Meinshausen, P Bühlmann The Annals of Statistics 34 (3), 1436-1462, 2006	4680	2006
Greenhouse-gas emission targets for limiting global warming to 2 C M Meinshausen, N Meinshausen, W Hare, SCB Raper, K Frieler, R Knutti, ... Nature 458 (7242), 1158-1162, 2009	3794	2009
Stability selection N Meinshausen, P Bühlmann Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2010	3084	2010
Quantile regression forests N Meinshausen The Journal of Machine Learning Research 7, 983-999, 2006	2311	2006
Warming caused by cumulative carbon emissions towards the trillionth tonne MR Allen, DJ Frame, C Huntingford, CD Jones, JA Lowe, M Meinshausen, ... Nature 458 (7242), 1163-1166, 2009	2060	2009
The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500 M Meinshausen, ZRJ Nicholls, J Lewis, MJ Gidden, E Vogel, M Freund, ... Geoscientific Model Development 13 (8), 3571-3605, 2020	1151*	2020
Causal inference by using invariant prediction: identification and confidence intervals J Peters, P Bühlmann, N Meinshausen Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2016	1143	2016
Lasso-type recovery of sparse representations for high-dimensional data N Meinshausen, B Yu The Annals of Statistics 37 (1), 246-270, 2009	1075	2009
Relaxed lasso N Meinshausen Computational Statistics & Data Analysis 52 (1), 374-393, 2007	985	2007
The effects of climate extremes on global agricultural yields E Vogel, MG Donat, LV Alexander, M Meinshausen, DK Ray, D Karoly, ... Environmental Research Letters 14 (5), 054010, 2019	725	2019
P-values for high-dimensional regression N Meinshausen, L Meier, P Bühlmann Journal of the American Statistical Association 104 (488), 1671-1681, 2009	478	2009
Historical greenhouse gas concentrations for climate modelling (CMIP6) M Meinshausen, E Vogel, A Nauels, K Lorbacher, N Meinshausen, ... Geoscientific Model Development 10 (5), 2057-2116, 2017	458	2017
High-dimensional inference: confidence intervals, p-values and R-software hdi R Dezeure, P Bühlmann, L Meier, N Meinshausen Statistical science, 533-558, 2015	334	2015
Invariant causal prediction for nonlinear models C Heinze-Deml, J Peters, N Meinshausen Journal of Causal Inference 6 (2), 20170016, 2018	313	2018
Climate change now detectable from any single day of weather at global scale S Sippel, N Meinshausen, EM Fischer, E Székely, R Knutti Nature climate change 10 (1), 35-41, 2020	285	2020
Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses N Meinshausen, J Rice The Annals of Statistics 34 (1), 373-393, 2006	263	2006
Causal structure learning C Heinze-Deml, MH Maathuis, N Meinshausen Annual Review of Statistics and Its Application 5 (1), 371-391, 2018	258	2018
Anchor regression: Heterogeneous data meet causality (vol 83, pg 215, 2021) D Rothenhausler, N Meinshausen, P Buhlmann, J Peters JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 83 …, 2021	255*	2021
JM, Vollmer, MK, and Wang, RHJ: The shared socioeconomic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geosci. Model Dev., 13, 3571–3605 M Meinshausen, ZRJ Nicholls, J Lewis, MJ Gidden, E Vogel, M Freund, ...	217	2020
Hierarchical testing of variable importance N Meinshausen Biometrika 95 (2), 265-278, 2008	212	2008

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