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Variable selection in sparse regression models is an important task as applications ranging from biomedical research to econometrics have shown.
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L. Mentch, G. Hooker, Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests, The Journal of Machine Learning Research 17 (1) (2016) 841–881
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B. Ramosaj, M. Pauly, Consistent estimation of residual variance with random forest Out-Of-Bag errors, Statistics & Probability Letters 151 (2019) 49–57
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2014
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E. Scornet, G. Biau, J.-P. Vert, Consistency of Random Forests, The Annals of Statistics 43 (4) (2015) 1716–1741
2015
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2019
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