• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.671-681
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• 1999

이항분포의 정규근사는 중심극한정리의 한 예로서 자주 언급되는데 정규근사를 하기 위한 시행회수 n과 성공률 p에 대한 판정기준들이 다수 제시되고 있는 데, 본 논문은 이러한 판정기준들에 대하여 제약조건의 강도와 평균오차한계를 비교, 검토하였다.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.05a
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• pp.1029-1034
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• 1995

IAEA(International Atomic Energy Agency, 국제원자력기구)에서는 사찰활동 수행시, 비복원추출을 기술하는 초기 하분포(hypergeometric distribution) 대신 복원추출을 기술하는 이항분포(binomial distribution)를 사용하여 표본크기 (sample site)를 계산하여 최대 3가지 검증방법들에 할당한다. 본 연구에서는 사찰표본할당과 관련하여 PC사용이 요구되는 반복할당법인 초기하할당법, 개선된 이항할당법, 그리고 표준할당법과 포켓계산기에서 사용 가능한 근사 할당법인 개선된 이항할당근사법과 표준이항할당근사법을 비교 검토하였다.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.1-11
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• 2003

일반적으로 모수에 대한 신뢰구간 추정량이 점 추정량보다 훨씬 더 선호되고 있으며 많이 알려져 있다. 그러나 이산형 분포의 경우에는 주로 대 표본 근사 이론에 입각한 근사 신뢰구간이 많이 사용되고 있다. 본 논문에서는 여러 가지 이산형 분포 가운데에서 가장 많이 활용되고 있는 이항분포와 포아송 분포의 모수에 대한 다양한 신뢰구간 추정량들을 소개하고 대 표본 근사 이론에 의한 신뢰구간뿐만 아니라 소 표본의 경우에도 유용하게 이용될 수 있는 신뢰구간 등을 살펴보고 이들 신뢰구간들을 비교하였다.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.05b
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• pp.1093-1098
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• 1995

IAEA(International Atomic Energy Agency, 국제원자력기구)에서는 사찰활동 수행시, 비복원추출(sampling without replacement)을 기술하는 초기하분포 대신 복원추출(sampling with replacement)을 기술하는 이항분포를 사용하여 표본크기를 계산하여 사찰방법들에 할당한다. 본 연구에서는 이항근사법이 사용되는 IAEA의 표본크기 할당계산결과와 이항근사법 대신 초기하분포를 적용한 IAEA표본크기 할당계산결과를 비교 검토하였다.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.341-353
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• 2008

The bootstrap method for the score test statistic is proposed in a bivariate negative binomial distribution. The Monte Carlo study shows that the score test for testing overdispersion underestimates the nominal significance level, while the score test for "intrinsic correlation" overestimates the nominal one. To overcome this problem, we propose a bootstrap method for the score test. We find that bootstrap methods keep the significance level close to the nominal significance level for testing the hypothesis. An empirical example is provided to illustrate the results.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1175-1182
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• 2011

In this paper, properties of exact confidence interval and some approximate confidence intervals 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 approximation of hypergeometirc distribution to the binomial and normal distribution respectively. Approximate confidence intervals based on these approximation are also adequately discussed. The performance of exact confidence interval estimates and approximate confidence intervals of hypergeometric parameter is compared in terms of actual coverage probability by small sample Monte Carlo simulation.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.615-623
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• 2011

Several methods are used in interval estimation for binomial proportion; however the coverage probabilities of most confidence intervals depart from the confidence level when the binomial population proportion closes to 0 or 1 due to the extreme value. Vollset (1993), Agresti and Coull (1998), Newcombe (1998), and Brown et al. (2001) suggested methods to adjust the extreme value. This paper discusses the influence of extreme value in a binomial confidence interval through the numerical comparison of 6 confidence intervals.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.607-615
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• 2005

Recently the interval estimation of a binomial proportion is revisited in various literatures. This is mainly due to the erratic behavior of the coverage probability of the will-known Wald confidence interval. Various alternatives have been proposed. Among them, Agresti-Coull confidence interval has been recommended by Brown et al. (2001) with other confidence intervals for large sample, say n $\ge$ 40. On the other hand, a noninformative Bayesian approach called Polya posterior often produces statistics with good frequentist's properties. In this note, an interval estimator is developed using weighted Polya posterior. The resulting interval estimator is essentially the Agresti-Coull confidence interval with some improved features. It is shown that the weighted Polys posterior produce an effective interval estimator for small sample size and a severely skewed binomial distribution.

• Journal of the Korean Magnetics Society
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• v.18 no.1
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• pp.36-38
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• 2008

We calculate the electron scattering amplitude with reduced angular momentum expansion(RAME) and compare it with the plane wave approximation. By using WKB approximation it is shown that the curvature correction factor given by RAME is originated from the source wave centrifugal potential energy. The factor also can be understood as an effective wave number correction factor in plane wave approximation. Angular momentum and its relationship with scattering amplitude is explicitly shown.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.183-194
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• 2007

The problem of calculating the sample sizes for comparing two independent binomial proportions is studied, when one of two probabilities or both are smaller than 0.05. The use of Whittemore(1981)'s corrected sample size formula for small response probability, which is derived based oB multiple logistic regression, demonstrates much larger sample sizes compared to those by the asymptotic normal method, which is derived for the comparison of response probabilities belonging to the normal range. Therefore, applied statisticians need to be careful in sample size determination with small response probability to ensure intended power during a planning stage of clinical trials. The results of this study describe that the use of the sample size formula in the textbooks might sometimes be risky.