分类数据的可忽略性
Ignorability for categorical data
摘要：
研究了不完全分类数据的似然推理中的可验证性问题。两个版本的粗随机假设（车）的区别，他们的兼容性与参数的独特性假设的调查和几个条件，不需要额外的参数独特性假设的可验证性。结果表明，汽车的假设有相当不同的影响，这取决于底层的完整数据模型是饱和的或参数。在后一种情况下，汽车假设可能与观察到的数据不一致。
英文摘要：
We study the problem of ignorability in likelihood-based inference from incomplete categorical data. Two versions of the coarsened at random assumption (car) are distinguished, their compatibility with the parameter distinctness assumption is investigated and several conditions for ignorability that do not require an extra parameter distinctness assumption are established. It is shown that car assumptions have quite different implications depending on whether the underlying complete-data model is saturated or parametric. In the latter case, car assumptions can become inconsistent with observed data.
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