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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.

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SummaryThis paper develops a Twenty20 cricket simulator for matches between sides belonging to the International Cricket Council. As input, the simulator requires the probabilities of batting outcomes which are dependent on the batsman, the bowler, the number of overs consumed and the number of wickets lost. The determination of batting probabilities is based on an amalgam of standard classical estimation techniques and a hierarchical empirical Bayes approach where the probabilities of batting outcomes borrow information from related scenarios. Initially, the probabilities of batting outcomes are obtained for the first innings. In the second innings, the target score obtained from the first innings affects the aggressiveness of batting during the second innings. We use the target score to modify batting probabilities in the second innings simulation. This gives rise to the suggestion that teams may not be adjusting their second innings batting aggressiveness in an optimal way. The adequacy of the simulator is addressed through various goodness-of-fit diagnostics.

This paper considers estimation under the Cox proportional hazards model with rightcensored event times in the presence of covariates missing not at random (MNAR). We propose an approach derived from likelihood estimation utilizing supplementary information. We show that available additional information not only helps to account appropriately for the missing covariates but also leads to estimation procedures which are natural and easy to implement. A medical example is used throughout the paper to motivate the problem and to illustrate the proposed methodology.

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