Bias-corrected random forests in regression

DOI10.1080/02664763.2011.578621OpenAlexW2037202266MaRDI QIDQ5126933

Yan Lu, Guoyi Zhang

Publication date: 21 October 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2011.578621

zbMATH Keywords

simulationregressionrandom forestsbias correctionmean-squared prediction error

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate ⋮ Two-level quantile regression forests for bias correction in range prediction ⋮ Unnamed Item ⋮ AN EFFECTIVE BIAS-CORRECTED BAGGING METHOD FOR THE VALUATION OF LARGE VARIABLE ANNUITY PORTFOLIOS ⋮ Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance ⋮ Bias corrections for random forest in regression using residual rotation

Uses Software

• randomForest

Cites Work

• Bagging predictors
• On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification
• Multivariate adaptive regression splines
• Arcing classifiers. (With discussion)
• Random Forests and Adaptive Nearest Neighbors
• Random forests
• Using iterated bagging to debias regressions