Ignorability in general incomplete-data models

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72 Scopus citations


SUMMARY: Rubin (1976) defined ignorability conditions for frequentist and Bayes/likelihood analyses of data subject to missing observations. More recently, Heitjan & Rubin (1991) and Heitjan (1993) generalised the Rubin model to encompass other forms of incompleteness, establishing ignorability conditions for Bayes/likelihood inferences only. This paper extends the Heitjan-Rubin model by explicitly defining the observed degree of coarseness as a data element. This permits the development of a frequentist theory, including a generalisation of 'missing completely at random', the frequentist ignorability condition for missing data. The model is applied in a number of incomplete-data problems of general interest.

Original languageEnglish (US)
Pages (from-to)701-708
Number of pages8
Issue number4
StatePublished - Dec 1 1994


  • Coarsened at random
  • Coarsened completely at random
  • Missing at random
  • Missing completely at random
  • Observed at random

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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