Optimization techniques for edit validation and data imputation - ARCHIVED

This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.

The paper deals with concerns regarding the problem of automatic detection and correction of inconsistent or out-of-range data in a general process of statistical data collection. The proposed approach is capable of handling both qualitative and quantitative values. The purpose of this new approach is to overcome the computational limits of the Fellegi-Holt method, while maintaining its positive features. As customary, data records must respect a set of rules in order to be declared correct. By encoding the rules with linear inequalities, we develop mathematical models for the problems of interest. As a first relevant point, by solving a sequence of feasibility problems, the set of rules itself is checked for inconsistency or redundancy. As a second relevant point, imputation is performed by solving a sequence of set-covering problems.