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A Review of Hot Deck Imputation for Survey Non-response

Author

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  • Rebecca R. Andridge
  • Roderick J. A. Little

Abstract

Hot deck imputation is a method for handling missing data in which each missing value is replaced with an observed response from a "similar" unit. Despite being used extensively in practice, the theory is not as well developed as that of other imputation methods. We have found that no consensus exists as to the best way to apply the hot deck and obtain inferences from the completed data set. Here we review different forms of the hot deck and existing research on its statistical properties. We describe applications of the hot deck currently in use, including the U.S. Census Bureau's hot deck for the Current Population Survey (CPS). We also provide an extended example of variations of the hot deck applied to the third National Health and Nutrition Examination Survey (NHANES III). Some potential areas for future research are highlighted. Copyright (c) 2010 The Authors. Journal compilation (c) 2010 International Statistical Institute.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:istatr:v:78:y:2010:i:1:p:40-64
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    References listed on IDEAS

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    1. Jae Kwang Kim, 2004. "Fractional hot deck imputation," Biometrika, Biometrika Trust, vol. 91(3), pages 559-578, September.
    2. David Haziza & Jean-Fran├žois Beaumont, 2007. "On the Construction of Imputation Classes in Surveys," International Statistical Review, International Statistical Institute, vol. 75(1), pages 25-43, April.
    3. Jae Kwang Kim & J. Michael Brick & Wayne A. Fuller & Graham Kalton, 2006. "On the bias of the multiple-imputation variance estimator in survey sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 509-521.
    4. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    5. Yves G. Berger & J. N. K. Rao, 2006. "Adjusted jackknife for imputation under unequal probability sampling without replacement," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 531-547.
    6. Guangyu Zhang & Roderick Little, 2009. "Extensions of the Penalized Spline of Propensity Prediction Method of Imputation," Biometrics, The International Biometric Society, vol. 65(3), pages 911-918, September.
    7. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    8. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, July.
    9. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    10. repec:mpr:mprres:4780 is not listed on IDEAS
    11. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    12. Schenker, Nathaniel & Taylor, Jeremy M. G., 1996. "Partially parametric techniques for multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 22(4), pages 425-446, August.
    13. J. K. Kim, 2002. "A note on approximate Bayesian bootstrap imputation," Biometrika, Biometrika Trust, vol. 89(2), pages 470-477, June.
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