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Diagnostics for multivariate imputations


  • Kobi Abayomi
  • Andrew Gelman
  • Marc Levy


We consider three sorts of diagnostics for random imputations: displays of the completed data, which are intended to reveal unusual patterns that might suggest problems with the imputations, comparisons of the distributions of observed and imputed data values and checks of the fit of observed data to the model that is used to create the imputations. We formulate these methods in terms of sequential regression multivariate imputation, which is an iterative procedure in which the missing values of each variable are randomly imputed conditionally on all the other variables in the completed data matrix. We also consider a recalibration procedure for sequential regression imputations. We apply these methods to the 2002 environmental sustainability index, which is a linear aggregation of 64 environmental variables on 142 countries. Copyright (c) 2008 Royal Statistical Society.

Suggested Citation

  • Kobi Abayomi & Andrew Gelman & Marc Levy, 2008. "Diagnostics for multivariate imputations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(3), pages 273-291.
  • Handle: RePEc:bla:jorssc:v:57:y:2008:i:3:p:273-291

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    Cited by:

    1. Morehart, Mitch & Milkove, Dan & Xu, Yang, 2014. "Multivariate Farm Debt Imputation in the Agricultural Resource Management Survey (ARMS)," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169401, Agricultural and Applied Economics Association.
    2. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    3. repec:liu:liucej:v:13:y:2016:i:2:p:135-167 is not listed on IDEAS
    4. Martin, Eisele & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," MPRA Paper 57666, University Library of Munich, Germany.
    5. Kilic, Talip & Zezza, Alberto & Carletto, Calogero & Savastano, Sara, 2017. "Missing(ness) in Action: Selectivity Bias in GPS-Based Land Area Measurements," World Development, Elsevier, vol. 92(C), pages 143-157.
    6. Breitwieser, Anja & Wick, Katharina, 2016. "What We Miss By Missing Data: Aid Effectiveness Revisited," World Development, Elsevier, vol. 78(C), pages 554-571.
    7. repec:eee:socmed:v:188:y:2017:i:c:p:157-165 is not listed on IDEAS
    8. Kobi Abayomi & Gonzalo Pizarro, 2013. "Monitoring Human Development Goals: A Straightforward (Bayesian) Methodology for Cross-National Indices," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(2), pages 489-515, January.
    9. Siddique, Juned & Harel, Ofer, 2009. "MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i09).
    10. Wesley Eddings & Yulia Marchenko, 2012. "Diagnostics for multiple imputation in Stata," Stata Journal, StataCorp LP, vol. 12(3), pages 353-367, September.
    11. Jörg Drechsler, 2011. "Multiple imputation in practice—a case study using a complex German establishment survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 1-26, March.
    12. Burns, Christopher & Prager, Daniel & Ghosh, Sujit & Goodwin, Barry, 2015. "Imputing for Missing Data in the ARMS Household Section: A Multivariate Imputation Approach," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205291, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    13. d'Agostino, Giorgio & Pieroni, Luca & Procidano, Isabella, 2016. "Revisiting the relationship between welfare spending and income inequality in OECD countries," MPRA Paper 72020, University Library of Munich, Germany.

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