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The Role of Artificial Observations in Testing for the Difference of Proportions in Misclassified Binary Data
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 Title & Authors
The Role of Artificial Observations in Testing for the Difference of Proportions in Misclassified Binary Data
Lee, Seung-Chun;
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 Abstract
An Agresti-Coull type test is considered for the difference of binomial proportions in two doubly sampled data subject to false-positive error. The performance of the test is compared with the likelihood-based tests. It is shown that the Agresti-Coull test has many desirable properties in that it can approximate the nominal significance level with compatible power performance.
 Keywords
Agrestt-Coull interval;double sampling;profile likelihood;Rao score;
 Language
English
 Cited by
1.
The Role of Artificial Observations in Misclassified Binary Data with Common False-Positive Error,;

응용통계연구, 2012. vol.25. 4, pp.697-706 crossref(new window)
1.
The Role of Artificial Observations in Misclassified Binary Data with Common False-Positive Error, Korean Journal of Applied Statistics, 2012, 25, 4, 697  crossref(new windwow)
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