The project will evaluate and compare the current methods for editing and imputation to establish current best practice methods. In addition, new methods for editing and imputation based on neural networks, support vector machine, fuzzy logic methodology and robust statistical methods will be developed and compared with the current best practice methods. The evaluation of different methods will require, (a) the creation of common data sets with known type of errors to be used by all the methods, and (b) the establishment of sound statistical criteria for the objective evaluation of the methods. Based on our evaluation, recommendations for the use of different methods for editing and imputation for different kinds of data sets will be made.
1. To establish a standard collection of data sets for EUREDIT
2. To develop a methodological evaluation framework and develop evaluation criteria
3. To establish a baseline by evaluating currently used methods for data editing and imputation.
4. To develop and evaluate a selected range of new techniques for data editing and imputation.
5. To evaluate different methods for edit and imputation and establish best methods for different types of data.
6. To disseminate the best methods via a single package for wider dissemination, and in a conference proceedings.