Abstract
Owing to its practicality as well as strong inferential properties, multiple imputation has been increasingly popular in the analysis of incomplete data. Methods that are not only computationally elegant but also applicable in wide spectrum of statistical incomplete data problems have also been increasingly implemented in a numerous computing environments. Unfortunately, however, the speed of this development has not been replicated in reaching to "sophisticated" users. While the researchers have been quite successful in developing the underlying software, documentation in a style that would be most reachable to the greater scientific society has been lacking. The main goal of this special volume is to close this gap by articles that illustrate these software developments. Here I provide a brief history of multiple imputation and relevant software and highlight the contents of the contributions. Potential directions for the future of the software development is also provided.
Highlights
Methods targeting missing values in a wide spectrum of statistical analyses are part of serious statistical thinking due to many advances in computational statistics and increased awareness among sophisticated consumers of statistics
This special volume will provide a unique forum on the current state of multiple imputation (MI) software targeting a wide audience including sophisticated consumers of statistics and statistical researchers
Manuscripts are organized following the underlying “imputation” philosophy implemented by the respective software
Summary
Methods targeting missing values in a wide spectrum of statistical analyses are part of serious statistical thinking due to many advances in computational statistics and increased awareness among sophisticated consumers of statistics. Regardless of the nature of the post-imputation phase, MI inference treats missing data as an explicit source of random variability and the uncertainty induced by this is explicitly incorporated into the overall uncertainty measures of the underlying inferential process. In 2002, SAS (SAS Institute Inc. 2003) implemented some of Schafer’s normal-based routines along with other routines for monotonic missingness patterns as well as matching-based MI routines (PROC MI and PROC MIANALYZE) This initiation by SAS was an important step in making the modern missing data techniques accessible and visible to a wider group of practitioners.
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