Ignorability in general incomplete-data models
Daniel F. Heitjan · Biometrika · 1994
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.