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http://dx.doi.org/10.5351/KJAS.2012.25.4.697

The Role of Artificial Observations in Misclassified Binary Data with Common False-Positive Error  

Lee, Seung-Chun (Department of Applied Statistics, Hanshin University)
Publication Information
The Korean Journal of Applied Statistics / v.25, no.4, 2012 , pp. 697-706 More about this Journal
Abstract
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.
Keywords
Agresti-Coull test; likelihood-based tests; profile likelihood; double sampling;
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Times Cited By KSCI : 3  (Citation Analysis)
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