Quantifying uncertainty of subsampling-based ensemble methods under a U-statistic framework

DOI10.1080/00949655.2022.2081969OpenAlexW4293077583MaRDI QIDQ5055266

Qing Wang, Yujie Wei

Publication date: 13 December 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2022.2081969

zbMATH Keywords

variance estimationU-statisticensembleunbiasedsubbaggingsub-random forest

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Linear regression; mixed models (62J05) Diagnostics, and linear inference and regression (62J20)

Uses Software

• randomForest

Cites Work

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• Bagging predictors
• Standard errors for bagged and random forest estimators
• Multivariate adaptive regression splines
• Analyzing bagging
• Rates of convergence for random forests via generalized U-statistics
• NOTES ON BIAS IN ESTIMATION
• Some properties of incomplete U-statistics
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Estimation and Accuracy After Model Selection
• Variance estimation of a general u-statistic with appllication to cross-validation
• A Class of Statistics with Asymptotically Normal Distribution
• The Theory of Unbiased Estimation
• Random Forest Prediction Intervals
• Random forests

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