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Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables

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  • White, Ian R.
  • Daniel, Rhian
  • Royston, Patrick

Abstract

Multiple imputation is a popular way to handle missing data. Automated procedures are widely available in standard software. However, such automated procedures may hide many assumptions and possible difficulties from the view of the data analyst. Imputation procedures such as monotone imputation and imputation by chained equations often involve the fitting of a regression model for a categorical outcome. If perfect prediction occurs in such a model, then automated procedures may give severely biased results. This is a problem in some standard software, but it may be avoided by bootstrap methods, penalised regression methods, or a new augmentation procedure.

Suggested Citation

  • White, Ian R. & Daniel, Rhian & Royston, Patrick, 2010. "Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2267-2275, October.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:10:p:2267-2275
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    1. Donald B. Rubin, 2003. "Nested multiple imputation of NMES via partially incompatible MCMC," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 3-18, February.
    2. Patrick Royston, 2007. "Multiple imputation of missing values: further update of ice, with an emphasis on interval censoring," Stata Journal, StataCorp LP, vol. 7(4), pages 445-464, December.
    3. Patrick Royston & John B. Carlin & Ian R. White, 2009. "Multiple imputation of missing values: New features for mim," Stata Journal, StataCorp LP, vol. 9(2), pages 252-264, June.
    4. Bull, Shelley B. & Mak, Carmen & Greenwood, Celia M. T., 2002. "A modified score function estimator for multinomial logistic regression in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 57-74, March.
    5. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
    6. Patrick Royston, 2005. "Multiple imputation of missing values: update," Stata Journal, StataCorp LP, vol. 5(2), pages 188-201, June.
    7. Patrick Royston, 2005. "Multiple imputation of missing values: Update of ice," Stata Journal, StataCorp LP, vol. 5(4), pages 527-536, December.
    8. Patrick Royston, 2009. "Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables," Stata Journal, StataCorp LP, vol. 9(3), pages 466-477, September.
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    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
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    8. 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.
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    15. Ferrari, Pier Alda & Annoni, Paola & Barbiero, Alessandro & Manzi, Giancarlo, 2011. "An imputation method for categorical variables with application to nonlinear principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2410-2420, July.
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