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Optimal Imputation of Erroneous Data: Categorical Data, General Edits

Author

Listed:
  • R. S. Garfinkel

    (University of Tennessee, Knoxville, Tennessee)

  • A. S. Kunnathur

    (University of Toledo, Toledo, Ohio)

  • G. E. Liepins

    (Oak Ridge National Laboratory, Oak Ridge, Tennessee)

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.

Suggested Citation

  • R. S. Garfinkel & A. S. Kunnathur & G. E. Liepins, 1986. "Optimal Imputation of Erroneous Data: Categorical Data, General Edits," Operations Research, INFORMS, vol. 34(5), pages 744-751, October.
    • Handle: RePEc:inm:oropre:v:34:y:1986:i:5:p:744-751
    DOI: 10.1287/opre.34.5.744
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    Cited by:

    1. George Petrakos & Claudio Conversano & Gregory Farmakis & Francesco Mola & Roberta Siciliano & Photis Stavropoulos, 2004. "New ways of specifying data edits," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 249-274, May.
    2. Sergio Delgado-Quintero & Juan-José Salazar-González, 2008. "A new approach for data editing and imputation," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 68(3), pages 407-428, December.

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