Inference and Missing Data

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Inference and Missing Data

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Title: Inference and Missing Data
Author: Rubin, Donald B.
Citation: Rubin, Donald B. 1976. Inference and missing data. Biometrika 63(3): 581-592.
Access Status: At the direction of the depositing author this work is not currently accessible through DASH.
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Abstract: When making sampling distribution inferences about the parameter of the data, {theta}, 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 {theta}, 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 {theta}. These conditions are the weakest general conditions under which ignoring the process that causes missing data always leads to correct inferences.
Published Version: http://dx.doi.org/10.1093/biomet/63.3.581
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:3408223

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  • FAS Scholarly Articles [7219]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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