Bayesian analysis of misclassified binomial data: double-sampling and the zero-numerator problem

Al-Kandari, Noriah M. and Garthwaite, Paul H. (2020). Bayesian analysis of misclassified binomial data: double-sampling and the zero-numerator problem. Communications in Statistics - Simulation and Computation (Early Access).

DOI: https://doi.org/10.1080/03610918.2020.1855448

Abstract

This article examines the zero numerator problem. This problem occurs when the misclassification rates are so low that both forms of misclassification may not both be present in the validation data. For this problem, the article compares two Bayesian methods for analyzing misclassified binary data that has a validation sub-study and three forms of a non-informative prior distribution. It shows that the two methods give similar results. However, the posterior distributions were sensitive to the choice of the prior distribution when misclassification rates are low. The article further presents a simulation study that reveals that the bias is small regardless of the considered prior distribution. However, when the misclassification rates are low, the coverage of credible intervals is markedly better for a Jeffreys’ prior than for either a Haldane’s or a uniform prior. Finally, the article highlights the merit of quantifying expert opinion to form a subjective prior distribution when the posterior distribution is sensitive to the prior.

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