Abstract
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.
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