Multiple imputation for model checking: Completed-data plots with missing and latent data

Andrew Gelman, Iven Van Mechelen, Geert Verbeke, Daniel F. Heitjan, Michel Meulders

Research output: Contribution to journalArticlepeer-review

99 Scopus citations


In problems with missing or latent data, a standard approach is to first impute the unobserved data, then perform all statistical analyses on the completed dataset - corresponding to the observed data and imputed unobserved data - using standard procedures for complete-data inference. Here, we extend this approach to model checking by demonstrating the advantages of the use of completed-data model diagnostics on imputed completed datasets. The approach is set in the theoretical framework of Bayesian posterior predictive checks (but, as with missing-data imputation, our methods of missing-data model checking can also be interpreted as "predictive inference" in a non-Bayesian context). We consider the graphical diagnostics within this framework. Advantages of the completed-data approach include: (1) One can often check model fit in terms of quantities that are of key substantive interest in a natural way, which is not always possible using observed data alone. (2) In problems with missing data, checks may be devised that do not require to model the missingness or inclusion mechanism; the latter is useful for the analysis of ignorable but unknown data collection mechanisms, such as are often assumed in the analysis of sample surveys and observational studies. (3) In many problems with latent data, it is possible to check qualitative features of the model (for example, independence of two variables) that can be naturally formalized with the help of the latent data. We illustrate with several applied examples.

Original languageEnglish (US)
Pages (from-to)74-85
Number of pages12
Issue number1
StatePublished - Mar 2005


  • Bayesian model checking
  • Exploratory data analysis
  • Multiple imputation
  • Nonresponse
  • Posterior predictive checks
  • Realized discrepancies
  • Residuals

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


Dive into the research topics of 'Multiple imputation for model checking: Completed-data plots with missing and latent data'. Together they form a unique fingerprint.

Cite this