Should we impute or should we weight? Examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables

DOI10.1016/j.csda.2017.05.006zbMath1466.62091OpenAlexW2620107220MaRDI QIDQ1658370

John J. McArdle, Timothy Hayes

Publication date: 14 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2017.05.006

zbMATH Keywords

missing datarandom forestsCARTclassification and regression treesnonnormalitysmall samples

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Related Items (1)

The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories

Uses Software

• R
• Mplus
• rpart
• lavaan
• MICE
• randomForest
• lavaan.survey
• bootstrap

Cites Work

• Unnamed Item
• Unnamed Item
• Bagging predictors
• A method for simulating non-normal distributions
• On structural equation modeling with data that are not missing completely at random
• Recursive partitioning for missing data imputation in the presence of interaction effects
• Fully conditional specification in multivariate imputation
• "Equivalent Sample Size" and "Equivalent Degrees of Freedom" Refinements for Inference Using Survey Weights Under Superpopulation Models
• Inference and missing data
• Multiple imputation of discrete and continuous data by fully conditional specification
• Random forests

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