Multiple Imputation in Practice: With Examples Using IVEware.

Multiple Imputation Using the Fully Conditional Specification Method: A Comparison of SAS®, Stata, IVEware, and R

This presentation emphasizes use of SAS 9.4 to perform multiple imputation of missing data using the PROC MI Fully Conditional Specification (FCS) method with subsequent analysis using PROC SURVEYLOGISTIC and PROC MIANALYZE. The data set used is based on a complex sample design. Therefore, the examples correctly incorporate the complex sample features and weights. The demonstration is then repe...

Multiple Imputation with Mplus

Practice of Epidemiology Strategies for Multiple Imputation in Longitudinal Studies

Multiple imputation is increasingly recommended in epidemiology to adjust for the bias and loss of information that may occur in analyses restricted to study participants with complete data (‘‘complete-case analyses’’). However, little guidance is available on applying the method, including which variables to include in the imputation model and the number of imputations needed. Here, the author...

Multiple Imputation Using Deep Denoising Autoencoders

Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness ...

Multimedia Retrieval Using Multiple Examples

The paper presents a variant of our generative probabilistic multimedia retrieval model that is suitable for information needs expressed as multiple examples. Results have been evaluated on the TRECVID 2003 collection.