This article considers multinomial data subject to misclassification in the presence of covariates which affect both the misclassification probabilities and the true classification probabilities. A subset of the data may be subject to a secondary measurement according to an infallible classifier. Computations are carried out in a Bayesian setting where it is seen that the prior has an important role in driving the inference. In addition, a new and less problematic definition of nonidentifiability is introduced and is referred to as hierarchical nonidentifiability.

中文翻译：

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带有协变量的误分类多项式数据的推断

本文认为在存在协变量的情况下容易遭受误分类的多项式数据会同时影响误分类概率和真实的分类概率。数据的子集可以根据可靠的分类器进行二次测量。计算是在贝叶斯环境中进行的，可以看出先验在推论中起着重要作用。另外，引入了一种新的且不太麻烦的不可识别性定义，该定义被称为分层不可识别性。