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Diagnostics for multivariate imputations

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  • Kobi Abayomi
  • Andrew Gelman
  • Marc Levy

Abstract

Summary. 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.

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, June.
    • Handle: RePEc:bla:jorssc:v:57:y:2008:i:3:p:273-291
    DOI: 10.1111/j.1467-9876.2007.00613.x
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    Cited by:

    1. Kilic,Talip & Yacoubou Djima,Ismael & Carletto,Calogero & Kilic,Talip & Yacoubou Djima,Ismael & Carletto,Calogero, 2017. "Mission impossible? exploring the promise of multiple imputation for predicting missing GPS-based land area measures in household surveys," Policy Research Working Paper Series 8138, The World Bank.
    2. Yang Zhao, 2022. "Diagnostic checking of multiple imputation models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 271-286, June.
    3. 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.
    4. Eduard Sariev & Guido Germano, 2019. "An innovative feature selection method for support vector machines and its test on the estimation of the credit risk of default," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 404-427, July.
    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. Regnerus, Mark, 2017. "Is structural stigma's effect on the mortality of sexual minorities robust? A failure to replicate the results of a published study," Social Science & Medicine, Elsevier, vol. 188(C), pages 157-165.
    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. Francesco Solfanelli & Emel Ozturk & Emilia Cubero Dudinskaya & Serena Mandolesi & Stefano Orsini & Monika Messmer & Simona Naspetti & Freya Schaefer & Eva Winter & Raffaele Zanoli, 2022. "Estimating Supply and Demand of Organic Seeds in Europe Using Survey Data and MI Techniques," Sustainability, MDPI, vol. 14(17), pages 1-23, August.
    10. 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).
    11. Wesley Eddings & Yulia Marchenko, 2012. "Diagnostics for multiple imputation in Stata," Stata Journal, StataCorp LP, vol. 12(3), pages 353-367, September.
    12. 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.
    13. Yulei He & Trivellore E. Raghunathan, 2012. "Multiple imputation using multivariate gh transformations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(10), pages 2177-2198, June.
    14. 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.
    15. 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.
    16. Roman Matkovskyy, 2016. "A comparison of pre- and post-crisis efficiency of OECD countries: evidence from a model with temporal heterogeneity in time and unobservable individual effect," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 13(2), pages 135-167, December.
    17. Eisele, Martin & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," EconStor Preprints 100007, ZBW - Leibniz Information Centre for Economics.
    18. repec:jss:jstsof:29:i09 is not listed on IDEAS
    19. Anja Breitwieser & Katharina Wick, 2013. "What We Miss By Missing Data: Aid Effectiveness Revisited," Vienna Economics Papers vie1302, University of Vienna, Department of Economics.
    20. 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.
    21. Zhong, Hua & Hu, Wuyang & Penn, Jerrod M., 2018. "Application of Multiple Imputation in Dealing with Missing Data in Agricultural Surveys: The Case of BMP Adoption," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(1), January.

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