Inference and missing data

1976/01/01 by Donald B. Rubin, DONALD B. RUBIN · 9,760 citations
Mathematics · #Advanced Statistical Methods and Models #Artificial intelligence #Bayesian probability #Computer science #Conditional probability distribution #Data mining #Econometrics #Imputation (statistics) #Inference #Mathematics #Missing data #Process (computing) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics

paper · doi:10.1093/biomet/63.3.581

published in Biometrika 63(3), 581-592 (Oxford University Press)

openalex publication_date 1976/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

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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