Bootstrap Inference with Stacked Multiple Imputations

Oregon Convention Center 

Stacked multiple imputation for missing data modifies the usual multiple imputation approach by stacking the m imputed data sets into a single data set for analysis. Various advantages of the stacked approach have been previously demonstrated (e.g., Beesley and Taylor, 2020 & 2021). We explore bootstrap approaches with stacked multiple imputation, similar to those suggested by Schomaker and Heumann (2018) for usual multiple imputation. We demonstrate that bootstrap inference with stacked multiple imputations has modest advantages in some settings with respect to computation and estimation.

Multiple Imputation

Stacked Multiple Imputation

Bootstrap

Missing Data 

Biometrics Section