Predictive mean matching imputation of semicontinuous variables
Predictive mean matching imputation of semicontinuous variables
复制标题
DOI：
10.1111/stan.12023
复制
发表时间：
2014-02-01
影响因子：
1.5
通讯作者：
van Buuren, Stef
中科院分区：
文献类型：
--
作者：
Vink, Gerko;Frank, Laurence E.;van Buuren, Stef
多重插补方法正确地解释了缺失数据的不确定性。用于创建多重插补的方法之一是预测均值匹配 (PMM)，这是一种通用方法。关于 PMM 在插补非正态半连续数据（点质量处于特定值且其他连续分布的偏斜数据）方面的性能知之甚少。我们通过在单变量和多变量缺失机制下进行模拟研究来研究 PMM 的性能以及用于插补半连续数据的专用方法。我们还研究了现实数据集上的性能。我们得出的结论是，PMM 性能至少与所研究的用于插补半连续数据的专用方法一样好，并且与其他方法相比，PMM 是产生合理插补并保留原始数据分布的唯一方法。
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.