• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.827-832
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• 1997

Statistical relations between a system and empirical distribution are studied in terms of the concept of Akaike's Information Criterion. From this consideration we derive a bootstrap criterion for determining the optimal number of hidden units in nerual networks.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2002.05a
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• pp.104-109
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• 2002

In this paper, a statistical analysis of fatigue crack growth behavior under constant amplitude loads has been carried out. Fatigue crack growth tests were conducted on sixteen pre-cracked compact tension (CT) specimens of the pressure vessel (SPV50) steel in controlled identical load and environmental conditions. The assessment of the statistical distribution of fatigue crack growth experimental data obtained from SPV50 steel was studied and also the correlation of the parameter C and m in the Paris-Erdogan law was discussed. The probability distribution function of fatigue crack growth life seems to follow the 3-parameter Weibull. The fatigue crack growth rate seems to follow the 3-parameter Weibull and the log-normal distribution. The coefficient of variation (COV) of fatigue crack growth life was observed to decrease as the crack grows. A strong negative linear correlation exists between the coefficient C and the exponent m in Paris model. Fatigue crack growth rate data shows a normal distribution for both m and logC.

• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.503-525
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• 2012

This study compared levels of mathematically talented students' statistical thinking with those of non-talented students in statistical modeling and sampling distribution understanding. t tests were conducted to test for statistically significant differences between mathematically gifted students and non-gifted students. In case of statistical modeling, for both of elementary and middle school graders, the t tests show that there is a statistically significant difference between mathematically gifted students and non-gifted students. Table of frequencies of each level, however, shows that levels of mathematically gifted students' thinking were not distributed at the high levels but were overlapped with those of non-gifted students. A similar tendency is also present in sampling distribution understanding. These results are thought-provoking results in statistics instruction for mathematically talented students.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.591-595
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• 2012

We frequently use Tukey's boxplot to identify outliers in the batch of observations of the continuous variable. In doing so, we implicitly assume that the underlying distribution belongs to the family of normal distributions. Such a practice of data handling is often superficial and improper, since in reality too many variables manifest the skewness. In this short paper, we build a modified boxplot and set the outlier identification procedure by assuming that the observations are generated from the skew normal distribution (Azzalini, 1985), which is an extension of the normal distribution. Statistical performance of the proposed procedure is examined with simulated datasets.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.559-568
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• 2018

Gene classification can involve complex order-restricted inference. Examining gene expression pattern across groups with order-restriction makes standard statistical inference ineffective and thus, requires different methods. For this problem, Roy's union-intersection principle has some merit. The M-estimator adjusting for outlier arrays in a microarray study produces a robust test statistic with distribution-insensitive clustering of genes. The M-estimator in conjunction with a union-intersection principle provides a nonstandard robust procedure. By exact permutation distribution theory, a conditionally distribution-free test based on the proposed test statistic generates corresponding p-values in a small sample size setup. We apply a false discovery rate (FDR) as a multiple testing procedure to p-values in simulated data and real microarray data. FDR procedure for proposed test statistics controls the FDR at all levels of ${\alpha}$ and ${\pi}_0$ (the proportion of true null); however, the FDR procedure for test statistics based upon normal theory (ANOVA) fails to control FDR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2A
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• pp.196-201
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• 2010

Measurement of Quality of Service (QoS) parameters and its statistical analysis becomes a key issue for Mobile WiMAX service providers to manage the converged network efficiently and to support end-to-end QoS. In this paper, we investigate the population distribution of end-to-end one-way delay which is the most important QoS parameter in Mobile WiMAX networks. The samples are analyzed with Chi-Square Goodness-of-Fit test, Kolmogorov-Smirnov (K-S), and Anderson-Darling (A-D) test to verify the distribution of parent population. The relation with confidence level and the minimum number of sample size is also performed for logistic distribution. The statistical analysis is a promising approach for measuring the performance Mobile WiMAX networks.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.385-410
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• 2019

This paper proposes a new class of distribution using the concept of exponentiated of distribution function that provides a more flexible model to the baseline model. It also proposes a new lifetime distribution with different types of hazard rates such as decreasing, increasing and bathtub. After studying some basic statistical properties and parameter estimation procedure in case of complete sample observation, we have studied point and interval estimation procedures in presence of type-II censored samples under a classical as well as Bayesian paradigm. In the Bayesian paradigm, we considered a Gibbs sampler under Metropolis-Hasting for estimation under two different loss functions. After simulation studies, three different real datasets having various nature are considered for showing the suitability of the proposed model.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.49-56
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• 1999

This paper present a study on the application of sound pressure and vibration signals to detect the presence of defects in a rolling element bearing using a statistical analysis method. The well established statistical parameters such as the crest factor and the distribution of moments including kurtosis and skewless are utilized in this study. In addition, other statistical parameters derived from the beta distribution function are also used. A comparison study on the performance of the different types of parameter used is also performed. The statistical analysis is used because of its simplicity and quick computation. Under ideal conditions, the statistical method can be used to identify the different types of defect present in the bearing. In addition, the results also reveal that there is no significant advantages in using the beta function parameters when compared to using kurtosis and the crest factor for detecting and identifying defects in rolling element bearings from both sound and vibration signals.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.4
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• pp.405-413
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• 2000

This paper present study on the application of sound pressure and vibration signals to detect the presence of defects in a rolling element bearing using a statistical analysis method. The well established statistical parameters such as the crest factor and the distribution of moments including kurtosis and skew are utilized in this study. In addition, other statistical parameters derived from the beta distribution function are also used. A comparison study on the performance of the different types of parameter used is also performed. The statistical analysis is used because of its simplicity and quick computation. Under ideal conditions, the statistical method can be used to identify the different types of defect present in the bearing. In addition, the results also reveal that there is no significant advantages in using the beta function parameters when compared to using kurtosis and the crest factor for detecting and identifying defects in rolling element bearings from both sound and vibration signals.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.645-652
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• 2016

The statistical modeling of input random variables is necessary in reliability analysis, reliability-based design optimization, and statistical validation and calibration of analysis models of mechanical systems. In statistical modeling methods, there are the Akaike Information Criterion (AIC), AIC correction (AICc), Bayesian Information Criterion, Maximum Likelihood Estimation (MLE), and Bayesian method. Those methods basically select the best fitted distribution among candidate models by calculating their likelihood function values from a given data set. The number of data or parameters in some methods are considered to identify the distribution types. On the other hand, the engineers in a real field have difficulties in selecting the statistical modeling method to obtain a statistical model of the experimental data because of a lack of knowledge of those methods. In this study, commonly used statistical modeling methods were compared using statistical simulation tests. Their advantages and disadvantages were then analyzed. In the simulation tests, various types of distribution were assumed as populations and the samples were generated randomly from them with different sample sizes. Real engineering data were used to verify each statistical modeling method.