State of the Multiple Imputation Software

Evaluation of software for multiple imputation of semi-continuous data.

It is now widely accepted that multiple imputation (MI) methods properly handle the uncertainty of missing data over single imputation methods. Several standard statistical software packages, such as SAS, R and STATA, have standard procedures or user-written programs to perform MI. The performance of these packages is generally acceptable for most types of data. However, it is unclear whether t...

Multiple imputation: review of theory, implementation and software.

Missing data is a common complication in data analysis. In many medical settings missing data can cause difficulties in estimation, precision and inference. Multiple imputation (MI) (Multiple Imputation for Nonresponse in Surveys. Wiley: New York, 1987) is a simulation-based approach to deal with incomplete data. Although there are many different methods to deal with incomplete data, MI has bec...

Multiple Imputation of Missing Values in Software Measurement Data

The value of knowledge inferred from information databases is critically dependent on the quality of data. We present multiple imputation as a reliable and consistent imputation technique for handling missing data in a numeric dependent variable in software metrics data sets. Experiments were conducted using multiple, mean, k-Nearest Neighbors, regression, and REPTree to impute missing values i...

Multiple Imputation with Mplus

Model - Based Multiple Imputation

managing Multiple imputation can improve data quality.