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- Title
Predictive mean matching imputation of semicontinuous variables.
- Authors
Vink, Gerko; Frank, Laurence E.; Pannekoek, Jeroen; Buuren, Stef
- Abstract
Multiple imputation methods properly account for the uncertainty of missing data. One of those methods for creating multiple imputations is predictive mean matching (PMM), a general purpose method. Little is known about the performance of PMM in imputing non-normal semicontinuous data (skewed data with a point mass at a certain value and otherwise continuously distributed). We investigate the performance of PMM as well as dedicated methods for imputing semicontinuous data by performing simulation studies under univariate and multivariate missingness mechanisms. We also investigate the performance on real-life datasets. We conclude that PMM performance is at least as good as the investigated dedicated methods for imputing semicontinuous data and, in contrast to other methods, is the only method that yields plausible imputations and preserves the original data distributions.
- Subjects
MULTIPLE imputation (Statistics); MISSING data (Statistics); SKEWNESS (Probability theory); SIMULATION methods &; models; STATISTICAL matching
- Publication
Statistica Neerlandica, 2014, Vol 68, Issue 1, p61
- ISSN
0039-0402
- Publication type
Article
- DOI
10.1111/stan.12023