The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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The Role of Artificial Observations in Misclassified Binary Data with Common False-Positive Error

  • Received : 2012.06.18
  • Accepted : 2012.08.03
  • Published : 2012.08.31
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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 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.

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References

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