• Journal of the Korean Data and Information Science Society
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• 제24권4호
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• pp.911-918
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• 2013

This study deals with the small sample likelihood based inference for the ratio of two normal variances. The small sample likelihood inference is an approximation method. The signed log-likelihood ratio statistic and the modified signed log-likelihood ratio statistic, which converge to standard normal distribution, are proposed for the normal variance ratio. Through the simulation study, the coverage probabilities of confidence interval and power of the exact, the signed log-likelihood and the modified signed log-likelihood ratio statistic will be compared. A real data example will be provided.

• 응용통계연구
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• 제12권2호
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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.

• 대한수학회지
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• 제46권1호
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• pp.83-97
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• 2009

Two new iterative algorithms are provided to find zeros of accretive operators in a Banach space E with a uniformly $G\hat{a}teaux$ differentiable norm. Strong convergence for two iterations is proved and as applications, the viscosity approximation results are obtained also.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.43-51
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• 1995

For the stress-strengh function, hierarchical Bayes estimations considered under squared error loss and entropy loss. In particular, the desired marginal postrior densities ate obtained via Gibbs sampler, an iterative Monte Carlo method, and Normal approximation (by Delta method). A simulation is presented.

• Communications for Statistical Applications and Methods
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• 제20권1호
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• pp.15-22
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• 2013

We find a sufficient condition for optimal Berry-Esseen bounds in the normal approximation of functionals of Gaussian fields studied by Nourdin and Peccati (2009b).

• 한국산업경영시스템학회:학술대회논문집
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• 한국산업경영시스템학회 2002년도 춘계학술대회
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• pp.359-364
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• 2002

In case of pn control chart often used in mass production system of plant industry and so on, we could evaluate it's performance by the approximation to normal distribution. It has many differences according to sample sizes and defective fraction, and have disadvantage that needs much samples to use the normal distribution approximation. Existent control charts can not detect the cause of process something wrong because it is taking the sampling intervals of fixed length about all times from the process. Therefore, to overcome this shortcoming we use VSI(variable sampling intervals) techniques in this paper. This technique takes a long sampling interval to have the next sampling point if the sample point is in stable state, and if the sample point is near control lines, it takes short sampling interval because the probability to escape control limit is high. To analyze performance of pn control charts that have existent fixed sampling intervals(FSI) and that use VSI technique, we compare ATS of two charts, and analyze the performance of each control chart by the sample sizes, process fraction defective and control limits that Ryan and Schwertman had proposed.

• 응용통계연구
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• 제23권6호
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• pp.1115-1124
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• 2010

두 비율비교 비열등성 임상시험(non-inferiority trial)에서의 검정은 두 비율간에 차이가 없다는 영가설에 근거한 정규근사 산출식이 흔히 사용 된다. 본 연구에서는 두 비율차이가 영이 아니라는 가설에서 유도된 Miettinen과 Nurminen이 제안한 우도 스코어검정(likelihood score test)과 Farrington과 Manning의 검정 그리고 비대칭도(skewness)을 보정한 Gart와 Nam이 제안한 검정방법을 소개하고 모의실험을 통하여 제 1종 오류와 검정력을 비교하여 비열등성 임상시험에서 검정방법의 결정에 가이드라인을 제시하고자 한다.

• Korea-Australia Rheology Journal
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• 제11권3호
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• pp.233-240
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• 1999

In order to understand the flow behavior of Electrorheological (ER) fluid, dynamic simulation has been intensively performed for the last decade. When the shear flow is applied, it is easy to carry out the simulation with relatively small number of particles because of the periodic boundary condition. For the squeezing flow, however, it is not easy to apply the periodic boundary condition, and the number of particles needs to be increased to simulate the ER system more realistically. For this reason, the simulation of ER fluid under squeezing flow has been mostly performed with some representative chains or with the approximation that severely restricts the flow geometry to reduce the computational load. In this study, Message Passing Interface (MPI), which is one of the most widely-used parallel processing techniques, has been employed in a dynamic simulation of ER fluid under squeezing flow. As the number of particles used in the simulation could be increased significantly, full domain between the electrodes has been covered. The numerical treatment or the approximation used to reduce the computational load has been evaluated for its validity, and was found to be quite effective. As the number of particles is increased, the fluctuation of the normal stress becomes diminished and the prediction in general was found to be qualitatively In good agreement with the experimental results.

• 산업경영시스템학회지
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• 제31권4호
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• pp.134-139
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• 2008

Most approaches for continuous review inventory problem need tables for loss function and cumulative standard normal distribution. Furthermore, it is time-consuming to calculate order quantity (Q) and reorder point (r) iteratively until required values are converged. The purpose of this paper is to develop a direct method to get the solution without any tables. We used approximation approaches for loss function and cumulative standard normal distribution. The proposed method can get the solution directly without any iterative procedure for Q, r and without any tables. The performance of the proposed approach is tested by using numerical examples. The budget constraint of this paper assumes that purchasing costs are paid at the time an order is arrived. This constraint can be easily replaced by capacity constraint or budget constraint in which' purchasing costs are paid at the time an order is placed.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.26-29
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• 2002

Radial Basis Function (RBF) networks is known as efficient method in classification problems and function approximation. The basis function of RBF networks is usual adopted normal distribution like the Gaussian function. The output of the Gaussian function has the maximum at the center and decrease as increase the distance from the center. For learning of neural network, the method treating the limited area of input space is sometimes more useful than the method treating the whole of input space. The q-normal distribution is the set of probability density function include the Gaussian function. In this paper, we introduce the RBF networks with the basis function of q-normal distribution and actually approximate a function using the RBF networks.