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Activity Number: 236
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract #320171
Title: Benchmarking and Assessment for Multiple Imputation
Author(s): Gerko Vink*
Companies: Utrecht University
Keywords: Multiple imputation ; Benchmarking ; Standardized evaluation ; Machine Learning
Abstract:

The increased popularity of multiple imputation to obtain valid inference on incomplete data has identified two potential problems. First, imputation techniques are developed to solve distinct problems. They are evaluated on their performance on these problems, but are potentially of great scientific use outside of their target application. Such innovative applications remain unknown. Second, the target audience for multiple imputation consists of applied researchers from all scientific domains. These researchers often lack the statistical knowledge to understand the methodology behind these imputation techniques. How can these researchers decide what imputation technique would be suitable for their problem?

Integrating benchmarking into multiple imputation methodology can solve these problems. When it is possible to compare benchmarked techniques to one another, we can determine what would be the most suitable imputation method for specific data problems. This allows applied researchers to get the optimal imputation technology for their data, without the advanced statistical knowledge that is normally required when implementing multiple imputation techniques.


Authors who are presenting talks have a * after their name.

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