Self-organising map for data imputation and correction in surveys

This paper is dedicated to erroneous data detection and imputation methods in surveys. vs. We describe experiments conducted under the scope of a European project for studying new statistical methods based on neural networks. We show that the self-organising map can be used successfully for these tasks. A self-organising map is calibrated according to the available observations, described through a set of correlated variables handled together. The map can then be used both to detect erroneous data and to impute values to partial observations. We apply these principles to a real size transport survey database. We show that the performance of our imputation model compares well to other classical methods, and that the use of a self-organising map for data correction provides a performing system,for data validation, data correction and data analysis.