随机缺失、似然可拓性和模型完全性
Missing at random, likelihood ignorability and model completeness
摘要：
本文对不完全数据分析中随机缺失（MAR）的关键概念作了进一步的探讨。按照通常的选择建模方法，我们设想两个模型的参数可分离：一个模型的响应的兴趣和模型的缺失数据机制（MDM）。如果响应模型是由一个完整的密度族给出的，那么忽略MDM的似然函数的频率论推断是有效的，当且仅当MDM是MAR。这个充要条件也更普遍地适用于粗糙数据的模型，如删失。给出的例子来显示的必要性的基本模型的完整性，这种等价举行。
英文摘要：
This paper provides further insight into the key concept of missing at random (MAR) in incomplete data analysis. Following the usual selection modelling approach we envisage two models with separable parameters: a model for the response of interest and a model for the missing data mechanism (MDM). If the response model is given by a complete density family, then frequentist inference from the likelihood function ignoring the MDM is valid if and only if the MDM is MAR. This necessary and sufficient condition also holds more generally for models for coarse data, such as censoring. Examples are given to show the necessity of the completeness of the underlying model for this equivalence to hold.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
