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Diagnostics for multivariate imputations

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  • Kobi Abayomi
  • Andrew Gelman
  • Marc Levy
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    Abstract

    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.

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    Bibliographic Info

    Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series C (Applied Statistics).

    Volume (Year): 57 (2008)
    Issue (Month): 3 ()
    Pages: 273-291

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    Handle: RePEc:bla:jorssc:v:57:y:2008:i:3:p:273-291

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    Cited by:
    1. Kobi Abayomi & Gonzalo Pizarro, 2013. "Monitoring Human Development Goals: A Straightforward (Bayesian) Methodology for Cross-National Indices," Social Indicators Research, Springer, vol. 110(2), pages 489-515, January.
    2. Anja Breitwieser & Katharina Wick, 2013. "What We Miss By Missing Data: Aid Effectiveness Revisited," Vienna Economics Papers 1302, University of Vienna, Department of Economics.
    3. Juned Siddique & Ofer Harel, . "MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors," Journal of Statistical Software, American Statistical Association, vol. 29(i09).
    4. Jörg Drechsler, 2011. "Multiple imputation in practice—a case study using a complex German establishment survey," AStA Advances in Statistical Analysis, Springer, vol. 95(1), pages 1-26, March.

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