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Preserving Logical Relations while Estimating Missing Values

Author

Listed:
  • Ton de Waal

    (Statistics Netherlands & Tilburg University)

  • Wieger Coutinho

    (Statistics Netherlands)

Abstract

Item-nonresponse is often treated by means of an imputation technique. In some cases, the data have to satisfy certain constraints, which are frequently referred to as edits. An example of an edit for numerical data is that the profit of an enterprise equals its turnover minus its costs. Edits place restrictions on the imputations that are allowed and hence complicate the imputation process. In this paper we explore an adjustment approach. This adjustment approach consists of three steps. In the first step, the imputation step, nearest neighbour hot deck imputation is used to find several pre-imputed values. In a second step, the adjustment step, these pre-imputed values are adjusted so the resulting records satisfy all edits. In a third step, the best donor record is selected. The adjusted record corresponding to that donor record is the final imputed record. In principle, a potential donor that is not the closest to the record to be imputed may still give the best results after adjustment. In this paper we therefore focus on the number of potential donor records that are considered in the imputation step.

Suggested Citation

  • Ton de Waal & Wieger Coutinho, 2017. "Preserving Logical Relations while Estimating Missing Values," Romanian Statistical Review, Romanian Statistical Review, vol. 65(3), pages 47-59, September.
    • Handle: RePEc:rsr:journl:v:65:y:2017:i:3:p:47-59
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    References listed on IDEAS

    as
    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.
    2. Jae Kwang Kim, 2011. "Parametric fractional imputation for missing data analysis," Biometrika, Biometrika Trust, vol. 98(1), pages 119-132.
    3. Jae Kwang Kim, 2004. "Fractional hot deck imputation," Biometrika, Biometrika Trust, vol. 91(3), pages 559-578, September.
    4. Coutinho Wieger & Waal Ton de & Shlomo Natalie, 2013. "Calibrated Hot-Deck Donor Imputation Subject to Edit Restrictions," Journal of Official Statistics, Sciendo, vol. 29(2), pages 299-321, September.
    5. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Nearest-neighbour Imputation; Edit restrictions; Linear programming; Data adjustment;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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