To tackle the Identifiability issue and to Estimate the Misclassification Parameters | Research Letter

ABSTRACT:

Cluster randomization studies have become more common in place of traditional trials that randomly assign participants one at a time when this method is impractical for theoretical, ethical, or practical reasons. In the setting of a complementary poison model with potentially misclassified data, we evaluate three interval estimators for binomial misclassification rates: one based on the wald statistic, another on the score statistic, and a third on the profile log-likelihood statistic. As a result of its improved power and lower type I error, the redesigned test comes highly recommended. Semiparametric testing of misclassification estimates Information on the parameters employed in g (x*, z) that underlie parametric models, misclassification, and model and identification-related problems