Optimal Imputation of Erroneous Data: Categorical Data, General Edits

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Abstract
Responses to surveys often contain large amounts of incorrect information. One option for dealing with the problem is to revise those erroneous responses that can be detected. Fellegi and Holt developed a model in which a response is modified to pass a set of edits with as little change as possible. The model is called Minimum Weighted Fields to Impute (MWFI) and is NP-hard for categorical data and general edits. We develop two algorithms for MWFI, based on set covering, and present computational experience.