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
type: Item
instance of: scholarly article
title: 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
zbMATH Open document ID: 1466.62091
author: Timothy Hayes
author: John J. McArdle
published in: Computational Statistics and Data Analysis
publication date: 2018-08-14T00:00:00Z
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 6917774
zbMATH Keywords: CART
zbMATH Keywords: classification and regression trees
zbMATH Keywords: random forests
zbMATH Keywords: small samples
zbMATH Keywords: missing data
zbMATH Keywords: nonnormality
describes a project that uses: Mplus
describes a project that uses: lavaan
describes a project that uses: bootstrap
describes a project that uses: rpart
describes a project that uses: R
describes a project that uses: lavaan.survey
describes a project that uses: MICE
describes a project that uses: randomForest
MaRDI profile type: Publication
full work available at URL: j.csda.2017.05.006
OpenAlex ID: W2620107220
cites work: Bagging predictors
cites work: Random forests
cites work: Q3327527
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cites work: Q4318617
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cites work: On structural equation modeling with data that are not missing completely at random
cites work: "Equivalent Sample Size" and "Equivalent Degrees of Freedom" Refinements for Inference Using Survey Weights Under Superpopulation Models
cites work: Inference and missing data
cites work: Multiple imputation of discrete and continuous data by fully conditional specification
cites work: Fully conditional specification in multivariate imputation
DOI: 10.1016/J.CSDA.2017.05.006
description: scientific article; zbMATH DE number 6917774