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Inference and missing data

1976/01/01 by Donald B. Rubin, DONALD B. RUBIN · 204 citations
Mathematics · #Statistical Methods and Bayesian Inference #Advanced Statistical Methods and Models #Statistical Methods and Inference

paper · doi:10.1093/biomet/63.3.581

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.

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