Multiple imputation of unordered categorical missing data: A comparison of the multivariate normal imputation and multiple imputation by chained equations

Simple item page
dc.contributor.authorKarangwa, Innocent
dc.contributor.authorKotze, Danelle
dc.contributor.authorBlignaut, Renette
dc.date.accessioned2023-02-13T07:43:31Z
dc.date.available2023-02-13T07:43:31Z
dc.date.issued2016
dc.description.abstract. Missing data are common in survey data sets. Enrolled subjects do not often have data recorded for all variables of interest. The inappropriate handling of them may negatively affect the inferences drawn. Therefore, special attention is needed when analysing incomplete data. The multivariate normal imputation (MVNI) and the multiple imputation by chained equations (MICE) have emerged as the best techniques to deal with missing data. The former assumes a normal distribution of the variables in the imputation model and the latter fills in missing values taking into account the distributional form of the variables to be imputed. This study examines the performance of these methods when data are missing at random on unordered categorical variables treated as predictors in the regression models. First, a survey data set with no missing values is used to generate a data set with missing at random observations on unordered categorical variables.en_US
dc.identifier.citationKarangwa, I. et al. (2016). Multiple imputation of unordered categorical missing data: A comparison of the multivariate normal imputation and multiple imputation by chained equations. Brazilian Journal of Probability and Statistics, 30(4), 521–539. 10.1214/15-BJPS292en_US
dc.identifier.issn0103-0752
dc.identifier.urihttp://hdl.handle.net/10566/8412
dc.language.isoenen_US
dc.publisherBrazilian Statistical Associationen_US
dc.subjectStatistics studiesen_US
dc.subjectDataen_US
dc.subjectImputationen_US
dc.subjectDemocratic Republic of Congoen_US
dc.titleMultiple imputation of unordered categorical missing data: A comparison of the multivariate normal imputation and multiple imputation by chained equationsen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
karangwa_multiple imputation of unordered categorical missing data_2016.pdf
Size:
205.11 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Research Articles (Statistics & Population Studies)