Abstract
Of interest in this paper is the development of a model that uses fixed, then inverse sampling of binary data that is subject to false-positive misclassification in an effort to estimate a proportion. From this model, both the proportion of success and false-positive misclassification rate may be estimated. Also, three first-order likelihood-based confidence intervals for the proportion of success are mathematically derived and studied via a Monte Carlo simulation. The simulation results indicate that the likelihood ratio interval is generally preferable over the Wald and score interval. Lastly, the model is applied to two different real-world medical data sets.
Acknowledgements
This work was supported, in part, by a research grant from the Science Technology Engineering Mathematics (STEM) Center at Stephen F. Austin State University.
Disclosure statement
No potential conflict of interest was reported by the author(s).