EUREDIT is investigating neural networks, support vector machines, model-based and donor-based editing and imputation methods. Of these, the first two represent technologies that have only recently become readily available, and offer promise for providing accurate imputation methods in situations where traditional methods run into difficulties. Model-based methods, particularly those based on multivariate models for the data generation process, will be investigated and further developed. Donor-based methods are already in place in a number of NSIs (National Statistical Institutes) and the project includes this methodology in order to provide a baseline for comparing the performance of the other more novel methods.

Although the coverage of methods and representative data sets in EUREDIT is quite broad, it is obviously impractical to attempt to cover all possible novel methods and data sets in detail with a realistic budget. For this reason, a certain amount of pre-selection has been performed during the preparatory work for the project. This was performed on the basis of previous experience with the methods among the partners, and a choice based on those that seem to show good potential for this application. This is justified because, although the coverage is not complete, EUREDIT will establish a framework applicable to future research projects in this field. Thus any "gaps" can be filled in the future using the data sets and methodological framework developed within EUREDIT to carry out similar evaluations using a new or different editing or imputation technique.

The Euredit work packages are (briefly) as follows: