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Simultaneous Edit-Imputation for Continuous Microdata

Author

Listed:
  • Hang J. Kim
  • Lawrence H. Cox
  • Alan F. Karr
  • Jerome P. Reiter
  • Quanli Wang

Abstract

Many statistical organizations collect data that are expected to satisfy linear constraints; as examples, component variables should sum to total variables, and ratios of pairs of variables should be bounded by expert-specified constants. When reported data violate constraints, organizations identify and replace values potentially in error in a process known as edit-imputation. To date, most approaches separate the error localization and imputation steps, typically using optimization methods to identify the variables to change followed by hot deck imputation. We present an approach that fully integrates editing and imputation for continuous microdata under linear constraints. Our approach relies on a Bayesian hierarchical model that includes (i) a flexible joint probability model for the underlying true values of the data with support only on the set of values that satisfy all editing constraints, (ii) a model for latent indicators of the variables that are in error, and (iii) a model for the reported responses for variables in error. We illustrate the potential advantages of the Bayesian editing approach over existing approaches using simulation studies. We apply the model to edit faulty data from the 2007 U.S. Census of Manufactures. Supplementary materials for this article are available online.

Suggested Citation

  • Hang J. Kim & Lawrence H. Cox & Alan F. Karr & Jerome P. Reiter & Quanli Wang, 2015. "Simultaneous Edit-Imputation for Continuous Microdata," Working Papers 15-44, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:15-44
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    File URL: https://www2.census.gov/ces/wp/2015/CES-WP-15-44.pdf
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    References listed on IDEAS

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    1. Hang J. Kim & Jerome P. Reiter & Quanli Wang & Lawrence H. Cox & Alan F. Karr, 2014. "Multiple Imputation of Missing or Faulty Values Under Linear Constraints," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 375-386, July.
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    Cited by:

    1. Hang J. Kim & Jörg Drechsler & Katherine J. Thompson, 2021. "Synthetic microdata for establishment surveys under informative sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 255-281, January.
    2. Orozco Vázquez Miguel, 2023. "Misallocation of Resources, Firm Characteristics, and Structural Factors: Evidence from Mexico," Working Papers 2023-11, Banco de México.
    3. Hang J. Kim & Jerome P. Reiter & Alan F. Karr, 2018. "Simultaneous edit-imputation and disclosure limitation for business establishment data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 63-82, January.

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    More about this item

    Keywords

    Bayesian; Economic; Editing; Missing; Mixture; Survey;
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