16: Integrated Likelihood CI for Binomial Proportion using Double Sampling under Misclassification

Thomas M. Menino Convention & Exhibition Center 

We consider the problem of interval estimating a binomial proportion parameter when data are subject to under-reporting using a double-sampling design. This double-sampling framework enables identifiability of all model parameters by incorporating additional information that increases the dimension of the sufficient statistics relative to the number of parameters. We focus on constructing two new confidence intervals (CIs) for the target proportion. We then contrast the performance of four current CIs with nuisance parameter and two proposed integrated likelihood (IL) CIs. Through extensive Monte Carlo simulations and two real-data examples, we evaluate the coverage probability and interval width of each method for many combinations of parameter settings and sample sizes. We demonstrate that an IL approach offers better nominal coverage and more stable CIs than five competing CIs.

Binary Data

False Positive Observations

Pseudo-Likelihood Methods 

Biometrics Section