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Theoretical Considerations for the Agresti-Coull Type Confidence Interval in Misclassified Binary Data
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 Title & Authors
Theoretical Considerations for the Agresti-Coull Type Confidence Interval in Misclassified Binary Data
Lee, Seung-Chun;
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 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;
 Language
Korean
 Cited by
1.
The Role of Artificial Observations in Testing for the Difference of Proportions in Misclassified Binary Data,;

응용통계연구, 2012. vol.25. 3, pp.513-520 crossref(new window)
1.
The Role of Artificial Observations in Testing for the Difference of Proportions in Misclassified Binary Data, Korean Journal of Applied Statistics, 2012, 25, 3, 513  crossref(new windwow)
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