• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.35-47
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• 2004

In this paper, we generalizes Kim and Bickel(2003)'s statistic for bivariate normality to that of multinormality, applying Fattorini(1986)'s method. Fattorini(1986) generalized Shapiro-Wilk's statistic for univariate normality to multivariate cases. The proposed statistic could be considered as an approximate statistic to Fattorini(1986)'s. It can be used even for a big sample size. Power performance of the proposed test is assessed in a Monte Carlo study.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.1-35
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• 2024

The assumption of homoscedasticity is one of the most crucial assumptions for many parametric tests used in the biological sciences. The aim of this paper is to compare the empirical probability of type I error and the power of ten parametric and two non-parametric tests for homoscedasticity with simulations under different types of distributions, number of groups, number of samples per group, variance ratio and significance levels, as well as through empirical data from an agricultural experiment. According to the findings of the simulation study, when there is no violation of the assumption of normality and the groups have equal variances and equal number of samples, the Bhandary-Dai, Cochran's C, Hartley's Fmax, Levene (trimmed mean) and Bartlett tests are considered robust. The Levene (absolute and square deviations) tests show a high probability of type I error in a small number of samples, which increases as the number of groups rises. When data groups display a nonnormal distribution, researchers should utilize the Levene (trimmed mean), O'Brien and Brown-Forsythe tests. On the other hand, if the assumption of normality is not violated but diagnostic plots indicate unequal variances between groups, researchers are advised to use the Bartlett, Z-variance, Bhandary-Dai and Levene (trimmed mean) tests. Assessing the tests being considered, the test that stands out as the most well-rounded choice is the Levene's test (trimmed mean), which provides satisfactory type I error control and relatively high power. According to the findings of the study and for the scenarios considered, the two non-parametric tests are not recommended. In conclusion, it is suggested to initially check for normality and consider the number of samples per group before choosing the most appropriate test for homoscedasticity.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.19-31
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• 2022

In Machine learning, we usually divide the entire data into training data and test data, train the model using training data, and use test data to determine the accuracy and generalization performance of the model. In the case of models with low generalization performance, the prediction accuracy of newly data is significantly reduced, and the model is said to be overfit. This study is about a method of generating training data based on central limit theorem and combining it with existed training data to increase normality and using this data to train models and increase generalization performance. To this, data were generated using sample mean and standard deviation for each feature of the data by utilizing the characteristic of central limit theorem, and new training data was constructed by combining them with existed training data. To determine the degree of increase in normality, the Kolmogorov-Smirnov normality test was conducted, and it was confirmed that the new training data showed increased normality compared to the existed data. Generalization performance was measured through differences in prediction accuracy for training data and test data. As a result of measuring the degree of increase in generalization performance by applying this to K-Nearest Neighbors (KNN), Logistic Regression, and Linear Discriminant Analysis (LDA), it was confirmed that generalization performance was improved for KNN, a non-parametric technique, and LDA, which assumes normality between model building.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.607-617
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• 2012

The goodness-of-fit test for multivariate normal distribution is important because most multivariate statistical methods are based on the assumption of multivariate normality. We propose goodness-of-fit test statistics for multivariate normality based on the modified squared distance. The empirical percentage points of the null distribution of the proposed statistics are presented via numerical simulations. We compare performance of several test statistics through a Monte Carlo simulation.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.639-646
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• 2015

We propose simultaneous tests for mean and variance under the normality assumption. After formulating the null hypothesis and its alternative, we construct test statistics based on the individual p-values for the partial tests with combining functions and derive the null distributions for the combining functions. We then illustrate our procedure with industrial data and compare the efficiency among the combining functions with individual partial ones by obtaining empirical powers through a simulation study. A discussion then follows on the intersection-union test with a combining function and simultaneous confidence region as a simultaneous inference; in addition, we discuss weighted functions and applications to the statistical quality control. Finally we comment on nonparametric simultaneous tests.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.2
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• pp.204-212
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• 2014

The purpose of this paper is to investigate the effects of calibration rounds on the statistical distribution of the muzzle velocity in acceptance test of propelling charge. It is shown that the normal distribution fits best among statistical distributions from goodness-of fit test. The 3p-Weibull distribution is also acceptable because the shape of the probability density function curve is similar to that of normal distribution and it also has near zero skewness value. Muzzle velocities of test rounds uncompensated by calibration rounds showed high variation and had comparatively higher skewness. Because the skewness of normal distribution is defined to be zero, calibration rounds make the normality of data higher.

• International Journal of Reliability and Applications
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• v.4 no.4
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• pp.171-181
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• 2003

This paper proposes a U-test statistic for the problem of testing that a life distribution is exponential against the alternative that it is harmonic new better (worse) than used in expectation upper tail HNBUET (HNWUET), but not exponential on complete data. Selected critical values are tabulated for sample sizes n =5(1)60. The asymptotic normality of the statistic is proved and a comparison is made of the asymptotic efficiency between the statistic and other statistics. The power of the test is studied by simulation. A test for HNBUET in the case of randomly right-censored data is also considered. An application of the proposed test statistic in medical sciences is given.

• KCI Concrete Journal
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• v.14 no.2
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• pp.76-80
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• 2002

The role of high volume Class F fly ash in reducing expansion due to Alkali-Silica Reaction (ASR) was investigated. A series of modified ASTM C 1260 tests were performed under three different levels of NaOH normality, extending the test period to 28 days, using high- or low alkali cement, and Class F fly ash up to 58 ％ by mass of cement. A reactive siliceous fine aggregate was used. The test results confirm that HVFA replacement in a cementitious system significantly helps in controlling expansion caused by ASR.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.309-318
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• 2006

There are many statistical methods of testing the equality of two population variances. Among them, the well-known F test is very sensitive to the normality assumption. Several other tests that do not assume normality have been proposed, but these tests usually need tables of critical values or software for hypotheses testing. McGrath and Yeh (2005) suggested a quick and compact Count Five test requiring only the calculation of the number of extreme points. Since the Count Five test uses only extreme values, this discards some information from the samples, often resulting in a degradation in power. In this paper, an alternative Count Five test using the trimmed mean is proposed and its properties are discussed for some distributions and normal mixtures.

• Journal of Korean Medicine Rehabilitation
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• v.33 no.1
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• pp.77-85
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• 2023

Objectives This study is designed to evaluate the safety of palmul-tang soft extract in healthy male volunteers. Methods Twelve healthy male volunteers were recruited. And this study was conducted in a single center. As a result of the laboratory test, the safety was evaluated by collecting vital signs of volunteers. Twelve subjects were assigned by serial number according to the registration order. For safety evaluation, blood samples were collected and vital signs were checked four times throughout the test period, including screening, pre-administration, post-administration (after 48 hours) and post-administration (after 7 days). The difference in variables was summarized as the mean±standard deviation. The normality was performed using Kolmogorov-Smirnov and Shapiro-Wilk test. If normality is satisfied, a paired t-test is applied. Otherwise, the Wilcoxon sign rank test, which is a nonparametric method, is applied. The significance was p<0.05. The incidence of all side effects is expressed as a percentage. Results In the case of red blood cell, hemoglobin, and hematocrit values, the result of normality test of variables for the difference value before and after administration is significant level p<0.05. However, all laboratory test values before and after administration did not deviate from the normal range. Also the deviations in the normal range could not be seen as significance related to this clinical trial. And no side effects related to clinical trial drugs were observed. Conclusions The soft extract of palmul-tang was considered safe for healthy male volunteers.