The Poisson assumption is popular when data arises in the form of counts. In many applications such counts are fallible. Little research has been done on the Poisson distribution when both false positives and false negatives are present. We present a model in this paper that corrects for misclassification of count data. Bayesian estimators are developed. We provide the actual posterior distributions via integration. Markov Chain Monte Carlo results, which are more convenient for large sample sizes, are utilized for inference.
No takes yet. Share an insight, caveat, or question.
Bratcher et al. (2002) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: