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A note on multiple imputation under complex sampling

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  • J. K. Kim
  • S. Yang

Abstract

SUMMARY Multiple imputation is popular for handling item nonresponse in survey sampling. Current multiple imputation techniques with complex survey data assume that the sampling design is ignorable. In this paper, we propose a new multiple imputation procedure for parametric inference without this assumption. Instead of using the sample-data likelihood, we use the sampling distribution of the pseudo maximum likelihood estimator to derive the posterior distribution of the parameters. The asymptotic properties of the proposed method are investigated. A simulation study confirms that the new procedure provides unbiased point estimation and valid confidence intervals with correct coverage properties whether or not the sampling design is ignorable.

Suggested Citation

  • J. K. Kim & S. Yang, 2017. "A note on multiple imputation under complex sampling," Biometrika, Biometrika Trust, vol. 104(1), pages 221-228.
    • Handle: RePEc:oup:biomet:v:104:y:2017:i:1:p:221-228.
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    File URL: http://hdl.handle.net/10.1093/biomet/asw058
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    References listed on IDEAS

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    1. Soubeyrand, Samuel & Haon-Lasportes, Emilie, 2015. "Weak convergence of posteriors conditional on maximum pseudo-likelihood estimates and implications in ABC," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 84-92.
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    Cited by:

    1. Gyuhyeong Goh & Jae Kwang Kim, 2021. "Accounting for model uncertainty in multiple imputation under complex sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 930-949, September.

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