Abstract
When making sampling distribution inferences about the parameter of the data, θ, it is appropriate to ignore the process that causes missing data if the missing data are 'missing at random' and the observed data are 'observed at random', but these inferences are generally conditional on the observed pattern of missing data. When making direct-likelihood or Bayesian inferences about θ, it is appropriate to ignore the process that causes missing data if the missing data are missing at random and the parameter of the missing data process is 'distinct' from θ. These conditions are the weakest general conditions under which ignoring the process that causes missing data always leads to correct inferences.
Keywords
Missing dataImputation (statistics)InferenceMathematicsStatisticsConditional probability distributionProcess (computing)Data miningBayesian probabilityEconometricsComputer scienceArtificial intelligence
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Publication Info
- Year
- 1976
- Type
- article
- Volume
- 63
- Issue
- 3
- Pages
- 581-592
- Citations
- 9337
- Access
- Closed
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Cite This
Donald B. Rubin
(1976).
Inference and missing data.
Biometrika
, 63
(3)
, 581-592.
https://doi.org/10.1093/biomet/63.3.581
Identifiers
- DOI
- 10.1093/biomet/63.3.581