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Stability Selection
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. ItExpand
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Quantile Regression Forests
  • N. Meinshausen
  • Mathematics, Computer Science
  • J. Mach. Learn. Res.
  • 1 December 2006
TLDR
Quantile regression forests give a non-parametric and accurate way of estimating conditional quantiles for high-dimensional predictor variables. Expand
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Greenhouse-gas emission targets for limiting global warming to 2 °C
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, impactsExpand
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Warming caused by cumulative carbon emissions towards the trillionth tonne
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 ofExpand
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LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA
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 ofExpand
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Relaxed Lasso
TLDR
The Lasso is an attractive regularisation method for high dimensional regression. Expand
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Causal inference using invariant prediction: identification and confidence intervals
TLDR
We propose a new framework for causal inference in which we collect all models that do show invariance in their predictive accuracy across settings and interventions. Expand
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p-Values for High-Dimensional Regression
Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically validExpand
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Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses
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 nullExpand
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High-Dimensional Inference: Confidence Intervals, $p$-Values and R-Software hdi
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 aExpand
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