Ignorability in General Incomplete-Data Models
本文扩展了Heitjan-Rubin模型，将观测到的粗糙度明确定义为数据元素，从而发展了频率学派理论，包括对缺失数据频率学派可忽略性条件的推广，并应用于多个常见的不完全数据问题。
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.