Physical Sciences and Mathematics Commons^™

A Fixed-Inverse Binary Misclassification Model Under Possible False-Positive Misclassification, Asmerom Tesfamichael, Kent Riggs

Bright Ideas Conference

In this project, we develop a particular statistical model for binary data that allows for the possibility of false-positive misclassification. To account for the misclassification, the model incorporates a two-stage sampling scheme.

• Next, we apply maximum likelihood methods to find estimators of the primary prevalence parameter p as well as the false-positive misclassification rate parameter ϕ. In addition, we derive confidence intervals for p based on inverting Wald, score and likelihood ratio statistics.

• Also, we graphically compare coverage and width properties of the Wald-based, score-based, and likelihood ratio-based confidence intervals for p through a Monte Carlo simulation. The …