Forecasting hurricane-related power outages via locally optimized random forests

DOI10.1002/STA4.346MaRDI QIDQ6541753

Kim Kaufeld, Lucas Mentch, Mary Frances Dorn, Tim Coleman

Publication date: 21 May 2024

Published in: Stat (Search for Journal in Brave)

zbMATH Keywords

importance samplingrandom forestsemi-supervised learningcovariate shift

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Title not available (Why is that?)
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• Customized training with an application to mass spectrometric imaging of cancer tissue
• Multivariate adaptive regression splines
• Improving predictive inference under covariate shift by weighting the log-likelihood function
• Quantifying uncertainty in random forests via confidence intervals and hypothesis tests
• A least-squares approach to direct importance estimation
• Input-dependent estimation of generalization error under covariate shift
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

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