Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Theoretical Considerations for the Agresti-Coull Type Confidence Interval in Misclassified Binary Data

오분류된 이진자료에서 Agresti-Coull유형의 신뢰구간에 대한 이론적 고찰

  • 이승천 (한신대학교 정보통계학과)
  • Received : 20110400
  • Accepted : 20110500
  • Published : 2011.07.31
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Abstract

Although misclassified binary data occur frequently in practice, the statistical methodology available for the data is rather limited. In particular, the interval estimation of population proportion has relied on the classical Wald method. Recently, Lee and Choi (2009) developed a new confidence interval by applying the Agresti-Coull's approach and showed the efficiency of their proposed confidence interval numerically, but a theoretical justification has not been explored yet. Therefore, a Bayesian model for the misclassified binary data is developed to consider the Agresti-Coull confidence interval from a theoretical point of view. It is shown that the Agresti-Coull confidence interval is essentially a Bayesian confidence interval.

표본추출에서 오분류된 이진자료는 흔히 발생될 수 있는 현실적인 문제이지만 통계적 방법론은 상대적으로 제한적이라고 할 수 있다. 특히, 모비율의 구간추정 문제는 고전적인 Wald 방법에 의존하고 있었다. 그러나 최근 이승천과 최병수 (2009)에서 Agresti-Coull 방법을 적용하고 새로운 구간추정 방법을 제시하였으며, 수치적인 방법에 의해 Agresti-Coull 신뢰구간의 효율성을 주장하였다. 본 연구에서는 오분류된 이진자료에 대한 베이지안 모형을 다루었으며, 베이지안 모형이 Agresti-Coull 신뢰구간의 이론적 배경이 될 수 있는지 살펴 보았다.

Keywords

References

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  1. The Role of Artificial Observations in Testing for the Difference of Proportions in Misclassified Binary Data vol.25, pp.3, 2012, https://doi.org/10.5351/KJAS.2012.25.3.513