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Parametric fractional imputation for missing data analysis

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  • Jae Kwang Kim

Abstract

Parametric fractional imputation is proposed as a general tool for missing data analysis. Using fractional weights, the observed likelihood can be approximated by the weighted mean of the imputed data likelihood. Computational efficiency can be achieved using the idea of importance sampling and calibration weighting. The proposed imputation method provides efficient parameter estimates for the model parameters specified in the imputation model and also provides reasonable estimates for parameters that are not part of the imputation model. Variance estimation is discussed and results from a limited simulation study are presented. Copyright 2011, Oxford University Press.

Suggested Citation

  • Jae Kwang Kim, 2011. "Parametric fractional imputation for missing data analysis," Biometrika, Biometrika Trust, vol. 98(1), pages 119-132.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:1:p:119-132
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    File URL: http://hdl.handle.net/10.1093/biomet/asq073
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    Citations

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    Cited by:

    1. Shu Yang & Jae Kwang Kim, 2016. "Likelihood-based Inference with Missing Data Under Missing-at-Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 436-454, June.
    2. Chen, Sixia & Haziza, David, 2023. "A unified framework of multiply robust estimation approaches for handling incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    3. Yijie Xue & Nicole Lazar, 2012. "Empirical likelihood-based hot deck imputation methods," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 629-646.
    4. Corder Nathan & Yang Shu, 2020. "Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 249-271, January.
    5. Corder Nathan & Yang Shu, 2020. "Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 249-271, January.
    6. Zhong, Ping-Shou & Chen, Sixia, 2014. "Jackknife empirical likelihood inference with regression imputation and survey data," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 193-205.
    7. Danhyang Lee & Jae Kwang Kim, 2022. "Semiparametric imputation using conditional Gaussian mixture models under item nonresponse," Biometrics, The International Biometric Society, vol. 78(1), pages 227-237, March.
    8. Seho Park & Jae Kwang Kim & Diana Stukel, 2017. "A measurement error model approach to survey data integration: combining information from two surveys," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 345-357, December.
    9. Olanrewaju Akande & Gabriel Madson & D. Sunshine Hillygus & Jerome P. Reiter, 2021. "Leveraging auxiliary information on marginal distributions in nonignorable models for item and unit nonresponse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 643-662, April.
    10. Damião Nóbrega Da Silva & Chris Skinner & Jae Kwang Kim, 2016. "Using Binary Paradata to Correct for Measurement Error in Survey Data Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 526-537, April.
    11. Lauren J. Beesley & Jeremy M. G. Taylor, 2021. "A stacked approach for chained equations multiple imputation incorporating the substantive model," Biometrics, The International Biometric Society, vol. 77(4), pages 1342-1354, December.
    12. Fang, Fang & Shao, Jun, 2016. "Iterated imputation estimation for generalized linear models with missing response and covariate values," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 111-123.
    13. 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.
    14. Hao Cheng & Ying Wei, 2018. "A fast imputation algorithm in quantile regression," Computational Statistics, Springer, vol. 33(4), pages 1589-1603, December.
    15. Weiping Zhang & Feiyue Xie & Jiaxin Tan, 2020. "A robust joint modeling approach for longitudinal data with informative dropouts," Computational Statistics, Springer, vol. 35(4), pages 1759-1783, December.
    16. Zhan Liu & Chun Yip Yau, 2022. "A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse," Statistical Papers, Springer, vol. 63(1), pages 317-342, February.
    17. Jingxuan Guo & Fuguo Liu & Wolfgang Karl Härdle & Xueliang Zhang & Kai Wang & Ting Zeng & Liping Yang & Maozai Tian, 2023. "Sampling Importance Resampling Algorithm with Nonignorable Missing Response Variable Based on Smoothed Quantile Regression," Mathematics, MDPI, vol. 11(24), pages 1-30, December.
    18. Jae Kwang Kim & Seho Park & Yilin Chen & Changbao Wu, 2021. "Combining non‐probability and probability survey samples through mass imputation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 941-963, July.

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