• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.71-86
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• 2005

Testing for normality has always been an important part of statistical methodology. In this paper a test statistic for multivariate normality is proposed. The underlying idea is to investigate all the possible linear combinations that reduce to the standard normal distribution under the null hypothesis and compare the order statistics of them with the theoretical normal quantiles. The suggested statistic is invariant with respect to nonsingular matrix multiplication and vector addition. We show that the limit distribution of an approximation to the suggested statistic is representable as the supremum over an index set of the integral of a suitable Gaussian process.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.905-926
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• 1998

We propose a modified mid P-value method to reduce the conservativeness for the inference of conditional associations in three-way contingency tables. This improves the ordinary mfd P-value method. For $2{\times} 2${\times} K$ tables, we propose confidence intervals for an assumed common odds ratio based on inverting two separate one-sided tests using the modified mid P-value. Also, an alternative and usually even better ways of constructing intervals, based on Inverting a two-sided test, are presented. The actual probability of coverage of a 100($1-\alpha$)% confidence interval is centered about the nominal level, but the modified mid P-value approach gives actual coverage probability even closer to the nominal level than the ordinary mid P-value approach.

• Journal of Applied Reliability
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• v.14 no.2
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• pp.93-96
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• 2014

RFID (Radio Frequency IDentification) system is recognition technology which can maintain various object's information. Reliability of RFID tags is the most important factor in RFID system. In this paper, we proposed ALT (Accelerated Life Test) method for UHF RFID tags. Temperature and humidity were adopted as stress factors and the accelerated life tests were conducted in three different conditions. We performed failure analysis for identifying failure mechanism and statistical analysis of test data. In the statistical analysis, we employed Inverse Power law for relationship between tag's life and stress. Through the statistical analysis, we proposed acceleration factor for several levels of temperature-humidity. The reliability qualification test plans were also designed for the tag's target reliability.

• Journal of Aerospace System Engineering
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• v.10 no.1
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• pp.103-110
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• 2016

This study shows the statistical properties of the domestic composite material prepregs test result. During the last three years(2012.5~2015.6) the prepreg specimen tests have been performed by referring to NCAMP developed test procedure which was approved by FAA. The database of (1) Carbon Tape, (2) Glass Fabric, and (3) Carbon Fabric composite material prepregs' characteristics have been established for certified aircraft structures. This qualified materials can be used for aircraft structural design through proper material equivalency procedures.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.187-196
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• 2002

Testing for normality has always been a center of practical and theoretical interest in statistical research. In this paper a test statistic for bivariate normality is proposed. The underlying idea is to investigate all the possible linear combinations that reduce to the standard normal distribution under the null hypothesis and compare the order statistics of them with the theoretical normal quantiles. The suggested statistic is invariant with respect to nonsingular matrix multiplication and vector addition. We show that the limit distribution of an approximation to the suggested statistic is represented as the supremum over an index set of the integral of a suitable Gaussian Process. We also simulate the null distribution of the statistic and give some critical values of the distribution and power results.

• Journal of the Korean Regional Science Association
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• v.11 no.1
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• pp.21-30
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• 1995

This paper deals with the forecasting models for traffic accident by region. Its objectives are to develop the appropriate model for projecting the accident and to analyze the regional characteristics of the accident model. The main results are as follow. First, the literature review, statistical tests and sensitivity analyses show that the joint model combined both PTM and Exponential functions is appropriate to project the traffic accidents by region. Second, the statistical analyses by region. Second, the statistical analyses on the regional accident models indicate that the levels of significance in terms of t-value, $R^2$ and F-value are very high. Finally, the comparative analyses among regions show that the regional differences on the accident patterns can be explained by the joint models and the accident indices (parameters, $P_{max}$, 1/b, $\eta$ etc.) of each region.

• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.217-223
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• 2010

The research proposes the complementary methodology using integrated hypothesis testing and confidence interval models that can be identified the statistical difference and practical equivalence. The models developed in this study can be used in the quality improvement processes such as QC story 15 steps. For the expressions of CI4LSD(Confidence Interval for Least Significant Difference) and CI4TOST(Confidence Interval for Two One-Sided Tests) are simple, quality practioners can efficiently handle them. CI4TOST models as a complement can be applied when CI4LSD models are influenced by sample size and precision.

• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.142-149
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• 1994

Substitution of graphic representation for extensive lists of numerical statistical data is highly desirable by both editors and readers of medical journals, faced with an exploding abundance of contemporary medical literature. A novel graphic tool. the 'double-ring diagram', is described herein which permits visual representation of information regarding certain statistical variables used to describe the performance of a test or physical sign in the diagnosis of a disease. The diagram is relatively easy to construct on the basis of a number of primary data such as the prevalence and the true positive, true negative. false positive and false negative test results. These values are reflected in the diagram along with the values of other statistical variables derived from them. such as the sensitivity. specificity, predictive values for positive and negative test result. and accuracy. This diagram may be useful in visualizing a test's performance and facilitating visual comparison of performance of two or more tests.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.1
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• pp.43-50
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• 2019

Statistical hypothesis tests are important for quantifying answers to questions about samples of data. The Step Process of Statistical Hypothesis Testing; state the null hypothesis, State the alternate hypothesis, State the alpha level, Find the z-score associated with alpha level, Find the test statistic using this formula, If the calculated t distribution value from the data is larger than the t distribution value of alpha level, then you are in the Rejection region and you can reject the Null Hypothesis with ($1-{\alpha}$) level of confidence.

• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.511-520
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• 2021

Over the last decade, Bitcoin has attracted a great deal of public interest and Bitcoin market has grown rapidly. One of the main characteristics of the market is that it often undergoes some events or incidents that cause outlying observations. To obtain reliable results in the statistical analysis of Bitcoin data, these outlying observations need to be carefully treated. In this study, we are interested in change point analysis for Bitcoin return series having such outlying observations. Since these outlying observations can affect change point analysis undesirably, we use a robust test for parameter change to locate change points. We report some significant change points that are not detected by the existing tests and demonstrate that the model allowing for parameter changes is better fitted to the data. Finally, we show that the model with parameter change can improve the forecasting performance of Value-at-Risk.