• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.231-240
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• 2016

Recently, fake test data of power cables in nuclear power plants was a terrible shock to the citizens. Some cable companies manipulated the test data to make unfair profits. In addition, fake test data cases were found in military supplies. The fake test data cases focused on parts of army's tank, armored car. This paper propose a new method that can detect fake test data using known statistical methods. In addition, the method was implemented in Microsoft Excel to allow easy use. Lastly, a check sheet was proposed to check the validity of the test system of military suppliers. By detecting and checking the fake test data, it is expected that our new method will play an important role in quality assurance of military supplies.

• Journal of Power System Engineering
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• v.2 no.2
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• pp.13-19
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• 1998

The purpose of study is to investigate the flame stability and Structure of turbulent diffusion flame behind the hollowed flame holder, which is located on the waste gas coming out from the test furnace. Fluctuating temperature and ion current measurement and their statistical treatment were used for the purpose. Three types of flame were stabilized and each of which were changed by adequate equivalence ration. And we found that have no periodicity near reacting zone.

• Korean Journal of Forensic Psychology
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• v.11 no.1
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• pp.21-36
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• 2020

In P300-based concealed information test (P300 CIT), it evaluates whether the P300 amplitude for the probe is significantly greater than that of the irrelevant to determine if the suspect is telling a lie. An independent sample t-test or a bootstrap method can be used as a statistical test to make that decision. Rosenfeld et al. (2004) used the bootstrap method, claiming that "t tests on single sweeps are too insensitive to use to compare mean probe and irrelevant P300s within individuals" and their method has been accepted to date. The purpose of the study is to evaluate whether the power of t-test is lower than that of the bootstrap method in the P300 CIT. The Monte Carlo study was conducted by using EEG collected from 39 participants. The results showed that the type I error rates of the t-test and the percentile bootstrap method were similar and the power of the percentile bootstrap method was slightly higher than that of the t-test. The type I error rates of the t-test and the percentile bootstrap method were slightly lower than the significance level and the powers of the two tests were also slightly lower than that of the theoretical t-test. On the other hand, the type I error rate and power of the standard error Bootstrap method were the same as those of the theoretical t-test and its power was .012 ~ .081 higher than that of t-test depending on experimental conditions.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1271-1284
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• 2011

The entropy-based test of fit for the inverse Gaussian distribution presented by Mudholkar and Tian(2002) can only be applied to the composite hypothesis that a sample is drawn from an inverse Gaussian distribution with both the location and scale parameters unknown. In application, however, a researcher may want a test of fit either for an inverse Gaussian distribution with one parameter known or for an inverse Gaussian distribution with both the two partameters known. In this paper, we introduce tests of fit for the inverse Gaussian distribution based on the Kullback-Leibler information as an extension of the entropy-based test. A window size should be chosen to implement the proposed tests. By means of Monte Carlo simulations, window sizes are determined for a wide range of sample sizes and the corresponding critical values of the test statistics are estimated. The results of power analysis for various alternatives report that the Kullback-Leibler information-based goodness-of-fit tests have good power.

• Food Science and Industry
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• v.52 no.1
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• pp.20-31
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• 2019

Sensory perception of food/beverage products is one of the most important quality factors to determine consumer acceptability and thus sensory discrimination methodology has been a vital tool for quality management. Signal detection theory(SDT) and Thurstonian modeling provide the most advanced psychometric approach to modeling various discrimination methods. In these theories, perceptual and cognitive decisional factors are considered so that, a fundamental measure of sensory difference (d') can be computed, independent of test methods used. In this paper, sensory discrimination analysis based on SDT and Thurstonian modeling is introduced for more accurate and systematic applications of sensory and hedonic quality management in industry. Ways to realize the statistical power and relative sensitivity of sensory discrimination methods theorized in SDT and Thurstonian modeling in practice, are also discussed by using a case study of the Nongshim quality management program for drinking water in which SDT A-Not A test methodology was further optimized.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.89-96
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• 2015

The mediation methods suggested by Baron and Kenny, Sobel, Aroian and Goodman, have widely used to test the mediating effect. However, as there are many problems in statistical test power, as well as statistical accuracy, a bootstrapping technique has been suggested as an alternative. In this paper, we adopt the phantom variables based on the bootstrapping technique to test the mediating effect in multiple mediator model consisting of three or more mediating variables. In particular, we formulate the multiple mediator model for analyzing the relations among organizational resources, the absorption capacity as mediating variables and technology commercialization capabilities. And using the bootstrapping approach, we analyzed the mediating effect of the absorption capacity by setting of phantom variables and calculated total indirect effect size and the statistical significance. The empirical results are as follows. First, we confirmed that the bootstrapping approach and the phantom variable is the very efficient and systematic mediation method. Second, we recognized that there is a difference in the mediating characteristics of the absorption capacity depending on the resource characteristics of human resources and material resources obviously.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.1033-1046
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• 2009

In new drug development studies or clinical trials, zero-dose control is needed in general to determine the lowest dose level for a new drug which can act with our bodies. When the lowest dose level compared with zero-dose control has significant difference in effect, it is referred as minimum effective dose(MED). We propose, in this paper, parametric sequential test using updated control to identify the minimum effective dose(MED) level. Monte Carlo Simulation is adapted to examine the power and experimental significance levels of the proposed method with other methods.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.115-125
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• 2009

Simultaneous equation models, which are widely used in business and economic areas, generally consist of endogenous variables determined within models and exogenous variables externally determined and in the simultaneous equations model framework there are rank and order conditions for the model identification and the existence of unique solutions. By contrast, their estimating results have less efficiencies and furthermore do not exist, since the most estimating procedures are performed under the assumptions for rank and order conditions. We propose the new statistical test for sufficiency of the rank condition under the order condition, and show the asymptotic properties for the test. The Monte Carlo simulation studies are achieved in order to evaluate its power and to suggest the baseline for satisfying the rank conditions.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1249-1263
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• 2009

The power function in sample size determination has to be characterized by an appropriate statistical test for the hypothesis of interest. Nonparametric tests are suitable in the analysis of ordinal data or frequency data with ordered categories which appear frequently in the biomedical research literature. In this paper, we study sample size calculation methods for the Wilcoxon-Mann-Whitney test for one- and two-dimensional ordinal outcomes. While the sample size formula for the univariate outcome which is based on the variances of the test statistic under both null and alternative hypothesis perform well, this formula requires additional information on probability estimates that appear in the variance of the test statistic under alternative hypothesis, and the values of these probabilities are generally unknown. We study the advantages and disadvantages of different sample size formulas with simulations. Sample sizes are calculated for the two-dimensional ordinal outcomes of efficacy and safety, for which bivariate Wilcoxon-Mann-Whitney test is appropriate than the multivariate parametric test.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.219-227
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• 2011

In this paper, we proposed a generalized bivariate negative binomial distribution allowing heterogeneous dispersions on two dependent variables based on a trivariate reduction technique. In this model, we propose the score and LR tests for testing the equality of dispersions and compare the efficiencies of the proposed tests using a Monte Carlo study. The Monte Carlo study shows that the proposed score and LR tests prove to be an efficient test for the equality of dispersions in the view of the significance level and power. However, the score test is easier to compute than the LR test and it shows a slightly better performance than the LR test from the Monte Carlo study, we suggest the use of score tests for testing the equality of dispersions on two dependent variables. In addition, an empirical example is provided to illustrate the results.