Parameter Subset Selection and Multiple Comparisons of Poisson Rate Parameters with Misclassification

We develop a fully Bayesian model for estimating a Poisson rate parameter subject to the presence of both false-positive and false-negative misclassification of the counts for a multiple sample scheme. We also propose decision-theoretic based parameter subset selection and multiple comparison methods for comparing Poisson rate parameters where counts are subject to misclassification. We apply our new data analysis methodologies to real-data examples. Probabilities used in these methods are approximated via Gibbs samplers