• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1557-1564
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• 2017

We take genomic sequences of high-dimensional low sample size (HDLSS) without ordering of response categories into account. When constructing an appropriate test statistics in this model, the classical multivariate analysis of variance (MANOVA) approach might not be useful owing to very large number of parameters and very small sample size. For these reasons, we present a pseudo marginal model based upon the Gini-Simpson index estimated via Bayesian approach. In view of small sample size, we consider the permutation distribution by every possible n! (equally likely) permutation of the joined sample observations across G groups of (sizes $n_1,{\ldots}n_G$). We simulate data and apply false discovery rate (FDR) and positive false discovery rate (pFDR) with associated proposed test statistics to the data. And we also analyze real SARS data and compute FDR and pFDR. FDR and pFDR procedure along with the associated test statistics for each gene control the FDR and pFDR respectively at any level ${\alpha}$ for the set of p-values by using the exact conditional permutation theory.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.1
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• pp.80-89
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• 2021

In Korea, marine fish landed at ports are randomly sampled on a periodic basis (e.g., daily or weekly), and body sizes (e.g., lengths and weights) of those sampled fish are measured. The motivation for our study is whether or not such measurements reflect the size distribution, especially the length distribution of fish landed (= a population), because such length measurements are key data for a length-based assessment model. The current sampling method is to sample fish landed at ports by body size group (e.g., very small, small, medium, large, very large), using the sampling weights as the number of boxes by body size group. In this study, we showed that length composition data about fish sampled by the current method did not represent the length frequency distribution of the fish landed, and suggested that an alternative sampling method should be applied of using the sampling weights as the number of fish landed by body size group. We also introduced a method for determining an appropriate sample size.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.3
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• pp.333-343
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• 1998

This paper proposes variable stream and sample size(VSSS) group control charts in which both the number of streams selected for sampling and sample size from each of the selected streams are allowed to vary based on the values of the preceding sample statistics. The proposed charts select a small portion of streams and take samples of size n = 1 if both the largest and smallest of sample means fall between the lower and upper threshold limits, and select a large portion of streams and take samples of size n > 1 otherwise. A Markov chain approach is used to derive the formulas for evaluating the performances of the proposed charts. Numerical comparisons are made between the VSSS and fixed stream and sample size(FSSS) group control charts.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.817-825
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• 2020

Over the last few decades, corporate frauds have highlighted the significance of corporate governance in deriving firm performance. By using different sample data, extensive research has examined how corporate governance structure influences firm's profitability, but limited research was undertaken on the banking sector of Pakistan. This research adds to the literature by testing how board structure derives bank's performance by using sample data of 19 banks for the period from 2010 to 2017. In addition, the study analyzes the controlling part of size on the link between board governance and bank performance. Findings reveal that banks having small board size, fewer non-executive directors and minimum activity level perform better. Analysis related to bank size illustrates that board size has value in increasing benefits in large size banks in contrast to small size one, while higher participation by board members enhances performance of small size banks more. The correlation results and findings showed that there existed no multicollinearity issue between independent variables. Board size showed positive correlation with the market variable, while board activity tended to correlated negatively with the market performance. Inverse correlation between board size and independent directors indicated that Pakistani banks with greater board size had fewer independent directors.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.115-120
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• 2015

Evaluating product quality level is necessary before the manufactured items are delivered to the customer. When the amount of the items to be manufactured is limited and the product is of high price and should be evaluated by destructive testing, the number of samples to be tested should be as small as possible. This paper presents a small-sample inspection method using hyper-geometric distribution and Bayesian approach for finite small-sized population. A method of determining the minimum sample size is presented for given population size, allowable number of defectives, warranteed defective level, and confidence level which is the degree of confidence on the product quality level recognized by both the producer and the customer.

• Clinics in Shoulder and Elbow
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• v.16 no.1
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• pp.53-57
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• 2013

Purpose: This review aims to explain the definition and basic principle of statistical analysis and to clarify statistical issues related to the sample size calculation. Materials and Methods: Many formulas are available that can be applied for different types of data and study design. Results: The sample size is the number of patients or other experimental units that need to be calculated prior to the study. Determining the appropriate sample size is required to answer the research question. Conclusion: Caution is needed when applying formula for the calculation of the sample size, as it is sensitive to error and even small differences in selected parameters can lead to large differences in the sample size.

• Korean Journal of Agricultural Science
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• v.37 no.3
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• pp.457-464
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• 2010

The low melting alloy impregnated wood composites with natural grain of thinned Juglans mandshurica was made and evaluated in this study. And the proper manufacturing conditions was also investigated in this study. The low melting alloy with bismuth(Bi) and tin(Sn) which are harmless to humans, was applied for this novel composites, which showed not only no defects of discoloration, delamination, swelling, and cracking, because of high dimensional stability and low thickness swelling, but also much improved performance such as high bending strength, high hardness, low abrasion, high thermal conductivity as floor materials. This study also suggested the proper impregnating condition, such as 10 minutes of the preliminary vacuum time, $187^{\circ}C$ of the heating temperature and 10 minutes of the maintaining pressure time at the pressure of 30kgf/$cm^2$. The produced composites showed 9 times higher density for small specimen, 6.6 times for actual size sample and great increase in bending strength from 102.05N/$mm^2$ to 189.47N/$mm^2$ for small size sample and to 205.4N/$mm^2$ for actual size sample, also great increase in hardness from 15.1N/$mm^2$ to 73.38N/$mm^2$ for small size sample and 64.87N/$mm^2$ for actual size sample. And the composites demonstrated great decrease in abrasion depth and in water absorption.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.172-173
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• 2008

LDA를 이용한 얼굴 인식에서 발생하는 small sample sire 문제를 해결하기 위해서 regularization method를 주로 사용한다. 이 방법을 사용하게 되면 클래스 내 분산행렬의 특이성을 없앨 수 있지만, 클래스 내 분산행렬과 단위행렬 $\alpha$를 곱한 값을 더하는 과정에서 $\alpha$의 값을 임의적으로 정해주어야 되고 이 값에 따라 인식률이 개선되지 않을 수 있다는 문제점이 있다. Resampling 개념을 이용하여 학습 데이터의 수를 늘리게 되면 regularization method보다 개선된 인식률을 얻을 수 있다. 또한 경험적으로 $\alpha$값을 정해 주어야 하고, $\alpha$값에 따라 인식률의 변통이 생길 수 있는 단점이 개선되는 효과를 얻을 수 있다.

• 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.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.595-610
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• 2013

Testing of order-restricted alternative hypothesis in $2{\times}k$ contingency tables can be applied to various fields of medicine, sociology, and business administration. Most testing methods have been developed based on a large sample theory. In the case of a small sample size or unbalanced sample size, the Type I error rate of the testing method (based on a large sample theory) is very different from the target point of 5%. In this paper, the exact testing method is introduced in regards to the testing of an order-restricted alternative hypothesis in categorical data (particularly if a small sample size or extreme unbalanced data). Power and exact p-value are calculated, respectively.