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Inference and Missing Data

Donald B. Rubin
Biometrika
Vol. 63, No. 3 (Dec., 1976), pp. 581-592
Published by: Oxford University Press on behalf of Biometrika Trust
DOI: 10.2307/2335739
Stable URL: http://www.jstor.org/stable/2335739
Page Count: 12
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Inference and Missing Data
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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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