Confidence interval construction for proportion difference from partially validated series with two fallible classifiers

ABSTRACT

This article investigates the confidence interval (CI) construction of proportion difference for two independent partially validated series under the double-sampling scheme in which both classifiers are fallible. Several CIs based on the variance estimates recovery method of combining confidence limits from asymptotic, bootstrap, and Bayesian methods for two independent binomial proportions are developed under two models. Simulation results show that all CIs except for the bootstrap percentile-t CI and Bayesian credible interval with uniform prior under the independence model and all CIs under the dependence model generally perform well and are recommended. Two examples are used to illustrate the methodologies.

KEYWORDS:

• Bootstrap resampling
• Bayesian method
• confidence interval
• fallible classifier
• method of variance estimates recovery

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China (Grant No. 11871124, 11471060) and the Natural Science Foundation of Chongqing (Grant No. cstc2018jcyjAX0241).