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The Role of Artificial Observations in Misclassified Binary Data with Common False-Positive Error
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 Title & Authors
The Role of Artificial Observations in Misclassified Binary Data with Common False-Positive Error
Lee, Seung-Chun;
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An Agresti-Coull type test is considered for the difference of binomial proportions in two doubly sampled data subject to common false-positive error. The performance of the test is compared with likelihood-based tests. The Agresti-Coull test has many desirable properties in that it can approximate the nominal significance level well, and has comparable power performance with a computational advantage.
Agresti-Coull test;likelihood-based tests;profile likelihood;double sampling;
 Cited by
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