On the actual coverage probability of hypergeometric parameter

초기하분포의 모수에 대한 신뢰구간추정

  • Kim, Dae-Hak (School of Liberal Arts, Catholic University of Daegu)
  • 김대학 (대구가톨릭대학교, 인성교양부)
  • Received : 2010.09.07
  • Accepted : 2010.10.25
  • Published : 2010.11.30

Abstract

In this paper, exact confidence interval of hyper-geometric parameter, that is the probability of success p in the population is discussed. Usually, binomial distribution is a well known discrete distribution with abundant usage. Hypergeometric distribution frequently replaces a binomial distribution when it is desirable to make allowance for the finiteness of the population size. For example, an application of the hypergeometric distribution arises in describing a probability model for the number of children attacked by an infectious disease, when a fixed number of them are exposed to it. Exact confidence interval estimation of hypergeometric parameter is reviewed. We consider the performance of exact confidence interval estimates of hypergeometric parameter in terms of actual coverage probability by small sample Monte Carlo simulation.

본 연구는 질병자료나 사망자수 등과 관련된 자료의 분석에서 가장 많이 사용되는 초기하분포의 모수, 즉 성공의 확률에 대한 신뢰구간추정에 대하여 설펴보았다. 초기하분포의 성공의 확률에 대한 신뢰구간은 일반적으로 잘 알려져 있지 않으나 그 응용성과 활용성의 측면에서 신뢰구간의 추정은 상당히 중요하다. 본 논문에서는 초기하분포의 성공의 확률에 대한 정확신뢰구간을 소개하고 여러 가지 모집단의 크기와 표본수에 대하여, 그리고 몇가지 실현값에 대한 신뢰구간을 유도하고 소표본의 경우에 모의실험을 통하여 실제 포함확률의 측면에서 살펴보았다.

Keywords

References

  1. Agresti, A. and Coull, B. A. (1998). Approximate is better than "Exact" for interval estimation of binomial proportions. The American Statistician, 52, 119-126. https://doi.org/10.2307/2685469
  2. Blyth, C .R. (1986). Approximate binomial confidence limits. Journal of the American Statistical Association, 81, 843-855. https://doi.org/10.2307/2289018
  3. Blyth, C. R. and Still, H. A. (1983). Binomial confidence intervals. Journal of the American Statistical Association, 78, 108-116. https://doi.org/10.2307/2287116
  4. Chen, H. (1990). The accuracy of approximate intervals for a binomial parameter. Journal of the American Statistical Association, 85, 514-518. https://doi.org/10.2307/2289792
  5. Clopper, C. J. and Pearson, E. S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26, 404-413. https://doi.org/10.1093/biomet/26.4.404
  6. Hald, A. (1952). Statistical theory with engineering applications, John Wiley, New York.
  7. IMSL. (1994). International mathematical and statistical libraries FORTRAN subroutines for evaluating special functions), Version 3, Visual Numerics, Houston, Texas.
  8. Katz, L. (1953). Confidence intervals for the number showing a certain characteristic in a population when sampling is without replacement. Journal of American Statistical Association, 48, 256-261. https://doi.org/10.2307/2281286
  9. Kim, D. (2010). On the actual coverage probability of binomial parameter. Journal of the Korean Data & Information Science Society, 21, 737-745.
  10. Leemis, L. M. and Trivedi, K. S. (1996). A comparison of approximate interval estimators for the bernoulli parameter. The American Statistician, 50, 63-68. https://doi.org/10.2307/2685046
  11. Liberman, G. J. and Owen, D. B. (1961). Tables of the hypergeometric probability distributions, Stanford University Press, Stanford.
  12. SAS. (1990). SAS language: Reference, Version 6, First Edition, SAS Institute, Cary, North Carolina.