期刊
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
卷 32, 期 3, 页码 285-302出版社
WILEY
DOI: 10.2307/3315930
关键词
convergence of Markov chains; Dirichlet priors; Gibbs sampling; latent variables; misclassification; nonidentifiability
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.
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