… us that they have read about multiple imputation (MI) but that … understanding of the theory
of multiple imputation, that is, what … behind the method of multiple imputation are explained in …

… In these data, applying multiple imputation by chained equations to the individual scale …
We propose an adaptation of multiple imputation by chained equations which imputes the …

… For comparison, we evaluate the performance of the various SRMI adaptations to imputation
using the “exact” imputation model in equation (2). This distribution may only be known up …

… imputed values could not be calculated with certainty. His solution was simple and brilliant:
create multiple imputations … the models and how to derive the imputations. A low number of …

… Multiple imputation is a widely recommended means of … that the imputation model used to
generate the imputed values … be taken into account in the imputation model. In the present …

… handling missing data using multiple imputation after reading this book! We also hope some
of the material can motivate readers to develop new multiple imputation ideas and methods. …

… of imputation methods to obtain multiple imputed training datasets. In general, the distributions
of each imputed … A covariance adaptation module (CAM) is then developed to reduce the …

… Based on these results, we conclude that multiple imputation should not be seen as the only
… In settings where multiple imputation is adopted, we recommend that imputation is carried …

… Whilst data imputation (particularly multiple imputation (MI)) is now generally accepted by
statisticians, non-specialist researchers have been slow to adopt it. Data imputation makes an …

… state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical
… several tools for managing and analyzing multiply imputed data sets. We illustrate the use of …