Regression with missing data, a comparison study of techniques based on random forests

DOI10.1080/00949655.2022.2163646arXiv2110.09333OpenAlexW3205227277MaRDI QIDQ6050720

Unnamed Author, Emilien Joly

Publication date: 19 September 2023

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

Full work available at URL: https://arxiv.org/abs/2110.09333

zbMATH Keywords

random forestsmissing valuesnon-parametric regressionprediction with missing values

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Bagging predictors
• Recursive partitioning on incomplete data using surrogate decisions and multiple imputation
• Impact of imputation of missing values on classification error for discrete data
• Multivariate adaptive regression splines
• A random forest guided tour
• Fully conditional specification in multivariate imputation
• Inference and missing data
• Local Linear Forests
• Random forests
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