Clarifying missing at random and related definitions, and implications when coupled with exchangeability: Table 1.
本文澄清了不完全数据分析中“随机缺失”的核心概念，区分了数据随机缺失与机制随机缺失，并讨论了后者与完全单位可交换性结合时对缺失机制建模的影响。
We clarify the key concept of missingness at random in incomplete data analysis. We first distinguish between data being missing at random and the missingness mechanism being a missing-at-random one, which we call missing always at random and which is more restrictive. We further discuss how, in general, neither of these conditions is a statement about conditional independence. We then consider the implication of the more restrictive missing-always-at-random assumption when coupled with full unit-exchangeability for the matrix of the variables of interest and the missingness indicators: the conditional distribution of the missingness indicators for any variable that can have a missing value can depend only on variables that are always fully observed. We discuss implications of this for modelling missingness mechanisms.