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On balanced random imputation in surveys

Author

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  • G. Chauvet
  • J.-C. Deville
  • D. Haziza

Abstract

Random imputation methods are often used in practice because they tend to preserve the distribution of the variable being imputed, which is an important property when the goal is to estimate population quantiles. However, this type of imputation method introduces additional variability, the imputation variance, due to the random selection of residuals. In this paper, we propose a class of random balanced imputation methods under which the imputation variance is eliminated while the distribution of the variable being imputed is preserved. The rationale behind balanced imputation is to select residuals at random so that appropriate constraints are satisfied. We describe an algorithm for selecting the random residuals that can be viewed as an adaptation of the cube algorithm proposed in the context of balanced sampling (Deville & Tille, 2004). Results of a simulation study support our findings. Copyright 2011, Oxford University Press.

Suggested Citation

  • G. Chauvet & J.-C. Deville & D. Haziza, 2011. "On balanced random imputation in surveys," Biometrika, Biometrika Trust, vol. 98(2), pages 459-471.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:2:p:459-471
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    File URL: http://hdl.handle.net/10.1093/biomet/asr011
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    Citations

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    Cited by:

    1. Caren Hasler & Radu V. Craiu, 2020. "Nonparametric imputation method for nonresponse in surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 25-48, March.
    2. Chauvet, Guillaume & Do Paco, Wilfried, 2018. "Exact balanced random imputation for sample survey data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 1-16.
    3. Helene Boistard & Guillaume Chauvet & David Haziza, 2016. "Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 683-699, September.
    4. Damião N. Da Silva & Li‐Chun Zhang, 2021. "A calibrated imputation method for secondary data analysis of survey data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 25-41, March.
    5. Hasler, Caren & Tillé, Yves, 2014. "Fast balanced sampling for highly stratified population," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 81-94.
    6. Torres Munguía, Juan Armando, 2014. "Comparison of Imputation Methods for Handling Missing Categorical Data with Univariate Pattern|| Una comparación de métodos de imputación de variables categóricas con patrón univariado," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 17(1), pages 101-120, June.
    7. Gelein, Brigitte & Haziza, David & Causeur, David, 2014. "Preserving relationships between variables with MIVQUE based imputation for missing survey data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 197-208.
    8. Leuenberger, Michael & Eustache, Esther & Jauslin, Raphaël & Tillé, Yves, 2022. "Balancing a sample almost perfectly," Statistics & Probability Letters, Elsevier, vol. 180(C).
    9. Yves Tillé, 2022. "Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials," International Statistical Review, International Statistical Institute, vol. 90(3), pages 481-498, December.

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