A new approach for data editing and imputation

The editing-and-imputation problem concerns the question of finding errors in a record which does not satisfy a set of consistency rules. Once some potential errors have been localizated, it is also necessary to impute new values to the associated fields. The output dataset should consist of valid records and preserve similar statistical properties as the input dataset. Most of this work is usually done manually by statistical agencies, thus consuming a great deal of human resources. This paper presents a mathematical programming model to optimally solve the problem on surveys with categorical values and particular edits. We also describe a heuristic approach to deal with the more complex surveys. The heuristic procedure follows a combination of the widely-accepted hot-deck donor scheme and the multivariate regression analysis. It has been implemented in a graphical user interface running on standard personal computers, and has been tested on real-world surveys. This paper demonstrates the satisfactory performance of our automatic procedure.