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http://dx.doi.org/10.5351/CKSS.2011.18.4.445

Theoretical Considerations for the Agresti-Coull Type Confidence Interval in Misclassified Binary Data  

Lee, Seung-Chun (Department of Statistics, Hanshin University)
Publication Information
Communications for Statistical Applications and Methods / v.18, no.4, 2011 , pp. 445-455 More about this Journal
Abstract
Although misclassified binary data occur frequently in practice, the statistical methodology available for the data is rather limited. In particular, the interval estimation of population proportion has relied on the classical Wald method. Recently, Lee and Choi (2009) developed a new confidence interval by applying the Agresti-Coull's approach and showed the efficiency of their proposed confidence interval numerically, but a theoretical justification has not been explored yet. Therefore, a Bayesian model for the misclassified binary data is developed to consider the Agresti-Coull confidence interval from a theoretical point of view. It is shown that the Agresti-Coull confidence interval is essentially a Bayesian confidence interval.
Keywords
Misclassified binary data; false-positive error; false-negative error; coverage probability; Agresti-Coull confidence interval;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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