Inference and missing data

Clicks: 2
ID: 289235
1976
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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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openalex_W2100358124 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Donald B. Rubin
Journal jurnal biometrika dan kependudukan
Year 1976
DOI
10.1093/biomet/63.3.581
URL
Keywords Keywords not found

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