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Calibrated imputation for multivariate categorical data

Author

Listed:
  • Ton Waal

    (Statistics Netherlands
    Tilburg University)

  • Jacco Daalmans

    (Statistics Netherlands)

Abstract

Non-response is a major problem for anyone collecting and processing data. A commonly used technique to deal with missing data is imputation, where missing values are estimated and filled in into the dataset. Imputation can become challenging if the variable to be imputed has to comply with a known total. Even more challenging is the case where several variables in the same dataset need to be imputed and, in addition to known totals, logical restrictions between variables have to be satisfied. In our paper, we develop an approach for a broad class of imputation methods for multivariate categorical data such that previously published totals are preserved while logical restrictions on the data are satisfied. The developed approach can be used in combination with any imputation model that estimates imputation probabilities, i.e. the probability that imputation of a certain category for a variable in a certain unit leads to the correct value for this variable and unit.

Suggested Citation

  • Ton Waal & Jacco Daalmans, 2024. "Calibrated imputation for multivariate categorical data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(3), pages 545-576, September.
    • Handle: RePEc:spr:alstar:v:108:y:2024:i:3:d:10.1007_s10182-023-00481-z
    DOI: 10.1007/s10182-023-00481-z
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    References listed on IDEAS

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    3. Chen, Sean X., 2000. "General Properties and Estimation of Conditional Bernoulli Models," Journal of Multivariate Analysis, Elsevier, vol. 74(1), pages 69-87, July.
    4. Hong, Yili, 2013. "On computing the distribution function for the Poisson binomial distribution," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 41-51.
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