응용통계연구 (The Korean Journal of Applied Statistics)

  • 제25권4호
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  • Pages.697-706
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  • 2012
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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The Role of Artificial Observations in Misclassified Binary Data with Common False-Positive Error

  • 투고 : 2012.06.18
  • 심사 : 2012.08.03
  • 발행 : 2012.08.31
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초록

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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참고문헌

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