Consistent estimation of residual variance with random forest out-of-bag errors

DOI10.1016/J.SPL.2019.03.017zbMath1459.62056arXiv1812.06270OpenAlexW2962791909MaRDI QIDQ2322623

Burim Ramosaj, Markus Pauly

Publication date: 5 September 2019

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1812.06270

zbMATH Keywords

consistencystatistical learningrandom forestresidual varianceout-of-bag samples

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Consistent and unbiased variable selection under indepedent features using random forest permutation importance ⋮ Is there a role for statistics in artificial intelligence? ⋮ Consistent regression using data-dependent coverings

Cites Work

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• On the asymptotics of random forests
• On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification
• Estimating residual variance in random forest regression
• Consistency of random forests
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Random Forests and Adaptive Nearest Neighbors
• Random forests

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