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Fractional hot deck imputation

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  • Jae Kwang Kim

Abstract

To compensate for item nonresponse, hot deck imputation procedures replace missing values with values that occur in the sample. Fractional hot deck imputation replaces each missing observation with a set of imputed values and assigns a weight to each imputed value. Under the model in which observations in an imputation cell are independently and identically distributed, fractional hot deck imputation is shown to be an effective imputation procedure. A consistent replication variance estimation procedure for estimators computed with fractional imputation is suggested. Simulations show that fractional imputation and the suggested variance estimator are superior to multiple imputation estimators in general, and much superior to multiple imputation for estimating the variance of a domain mean. Copyright Biometrika Trust 2004, Oxford University Press.

Suggested Citation

  • Jae Kwang Kim, 2004. "Fractional hot deck imputation," Biometrika, Biometrika Trust, vol. 91(3), pages 559-578, September.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:3:p:559-578
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    Citations

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    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. Xiaojun Mao & Zhonglei Wang & Shu Yang, 2023. "Matrix completion under complex survey sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 463-492, June.
    5. Frank Potter & Eric Grau & John Czajka & Dan Scheer & Mark Levitan, "undated". "Imputation Variance Estimation Protocols for the NAS Poverty Measure: The New York City Poverty Measure Experience," Mathematica Policy Research Reports 77be49e0f91f41e888de5139e, Mathematica Policy Research.
    6. Skinner, Chris J., 2007. "Discussion of J.F.Bjørnstad, ‘Non-Bayesian multiple imputation’," LSE Research Online Documents on Economics 39107, London School of Economics and Political Science, LSE Library.
    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. 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.
    9. repec:mpr:mprres:6788 is not listed on IDEAS
    10. 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.
    11. 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.
    12. Qin, Yongsong & Rao, J.N.K. & Ren, Qunshu, 2008. "Confidence intervals for marginal parameters under fractional linear regression imputation for missing data," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1232-1259, July.
    13. Gabriele Beissel Durrant, 2009. "Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review," Working Papers id:2007, eSocialSciences.
    14. 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.
    15. 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.
    16. Paolo Righi & Stefano Falorsi & Andrea Fasulo, 2014. "Methods for variance estimation under random hot deck imputation in business surveys," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 16(1-2), pages 45-64.
    17. 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.
    18. Shin-Soo Kang & Kenneth Koehler & Michael Larsen, 2012. "Fractional imputation for incomplete two-way contingency tables," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(5), pages 581-599, July.
    19. Edgar C. Merkle, 2011. "A Comparison of Imputation Methods for Bayesian Factor Analysis Models," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 257-276, April.

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