A Bayesian approach to obtain confidence intervals for binomial proportion in a double sampling scheme subject to false-positive misclassification

Abstract

The construction of a confidence interval based on a Bayesian approach is considered for the population proportion with double sampled data subject to false-positive misclassification. The Bayesian confidence intervals are compared with a standard frequentist confidence interval in terms of coverage probability and expected width. It is shown that a noninformative Bayes approach provides a relatively simple but effective confidence interval.