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The authors consider the Bayesian analysis of multinomial data in the presence of misclassification. Misclassification of the multinomial cell entries leads to problems of identifiability which are categorized into two types. The first type, referred to as the permutation‐type nonidentifiabilities, may be handled with constraints that are suggested by the structure of the problem. Problems of identifiability of the second type are addressed with informative prior information via Dirichlet distributions. Computations are carried out using a Gibbs sampling algorithm.

Discrepancy measures, generalized linear mixed model, Monte Carlo tests, sampling-based model fitting, stagewise model adequacy, 62A15, 62E25, 62-07,

This paper considers the analysis of round robin interaction data whereby individuals from a group of subjects interact with one-another producing a pair of outcomes, one for each individual. We provide an overview of the various analyses applied to round robin interaction data and extend the work in several directions. In particular, we provide a fully Bayesian analysis for round robin interaction data. A real data example is used for illustration. R ESUM E This paper considers the analysis of round robin interaction data whereby individuals from a group of subjects interact with one-another producing a pair of outcomes, one for each individual. We provide an overview of the various analyses applied to round robin interaction data and extend the work in several directions. In particular, we provide a fully Bayesian analysis for round robin interaction data. A real data example is used for illustration.

No abstract