Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate

DOI10.1080/10618600.2020.1820345OpenAlexW2789461502MaRDI QIDQ5066399

Indrayudh Ghosal, Giles Hooker

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1803.08000

zbMATH Keywords

bias reduction\(U\)-statisticsinferencerandom forestsvariance estimationboosting

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Unnamed Item

Uses Software

• UCI-ml
• AdaBoost.MH

Cites Work

• Greedy function approximation: A gradient boosting machine.
• Bootstrap bias corrections for ensemble methods
• Variable importance in binary regression trees and forests
• Random Forests and Kernel Methods
• Asymptotic Statistics
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Estimation and Accuracy After Model Selection
• Bias-corrected random forests in regression
• A Class of Statistics with Asymptotically Normal Distribution
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate