• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.23-30
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• 2017

Since shape, size and distribution of particles in particulate composites have spreaded characteristics, properties of particulate composites have variation and also system behavior using particulate composites have variation. However, it is difficult to consider spreaded characteristic of particles so that a system behavior is analysed using homogeneous techniques or using microstructure in local areas. In this study, for considering random variation of particles, RMDFs(random morphology description functions) are used to generate random microstructure and relationship between the number of gaussian functions and spreaded characteristic of particles was analysed using the geometrical moment of area. Also, multi-scale analysis was carried out for cantilever beam with full-random microstructure to study behavior of particulate composites structure. As a result, it is defined that spreaded characteristic of particles and the variation of deflections of cantilever beam are decreased as the number of Gaussian functions(N) is increased and converges at N=200.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.3
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• pp.281-288
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• 2016

The ballistic performance data of composite materials is distributed due to material inhomogeneity. In this paper, the uncertainty in residual velocity is obtained experimentally, and a method of predicting it is established numerically for the high-speed impact of a bullet into laminated composites. First, the failure strain distribution was obtained by conducting a tensile test using 10 specimens. Next, a ballistic impact test was carried out for the impact of a fragment-simulating projectile (FSP) bullet with 4ply ([0/90]s) and 8ply ([0/90/0/90]s) glass fiber reinforced plastic (GFRP) plates. Eighteen shots were made at the same impact velocity and the residual velocities were obtained. Finally, simulations were conducted to predict the residual velocities by using the failure strain distributions that were obtained from the tensile test. For this simulation, two impact velocities were chosen at 411.7m/s (4ply) and 592.5m/s (8ply). The simulation results show that the predicted residual velocities are in close agreement with test results. Additionally, the modeling of a composite plate with layered solid elements requires less calculation time than modeling with solid elements.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.493-504
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• 2017

We often observe count data that exhibit over-dispersion, originating from too many zeros, and under-dispersion, originating from too few zeros. To handle this types of problems, the zero-altered distribution model is designed by Ghosh and Kim in 2007. Their model can control both over-dispersion and under-dispersion with a single parameter, which had been impossible ever. The dispersion type depends on the sign of the parameter ${\delta}$ in zero-altered distribution. In this study, we demonstrate the role of the dispersion type parameter ${\delta}$ through the data of the number of births in Korea. Employing both the chi-square statistic and the Kolmogorov statistic for goodness-of-fit, we also explained any difference between the theoretical distribution and the observed one that exhibits either over-dispersion or under-dispersion. Finally this study shows whether the test statistics for goodness-of-fit show any similarity with the role of the dispersion type parameter ${\delta}$ or not.

• The Korean Journal of Applied Statistics
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• v.8 no.2
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• pp.147-161
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• 1995

The primary objective of this paper is to review parametric models and test statistics related to overdspersion of count data. Poisson or binomial assumption often fails to explain overdispersion. We reviewed real examples of overdispersion in count data that occurred in toxicological or teratological experiments. We also reviewed several models that were suggested for implementing experiments. We also reviewed several models that were suggested for implementing the extra-binomial variation or hyper-Poisson variability, and we noted how these models were generalized and further developed. The approaches that have been suggested for the overdispersion fall into two broad categories. The one is to develop a parametric model for it, and the other is to assume a particular relationship between the variance and the mean of the response variable and to derive a score test staistics for detecting the overdispersion. Recently, Dean(1992) derived a general score test statistics for detecting overdispersion from the exponential family.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.341-353
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• 2008

The bootstrap method for the score test statistic is proposed in a bivariate negative binomial distribution. The Monte Carlo study shows that the score test for testing overdispersion underestimates the nominal significance level, while the score test for "intrinsic correlation" overestimates the nominal one. To overcome this problem, we propose a bootstrap method for the score test. We find that bootstrap methods keep the significance level close to the nominal significance level for testing the hypothesis. An empirical example is provided to illustrate the results.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.513-521
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• 2014

If the target error of an estimator at the present time is greater than the coefficient of variation(CV) of the estimator at the previous time, sample size at this point should be decreased. Various papers have researched sample size determination methods using the CV of an estimator at the previous time, variation of population size and target error of the estimator at this time in sampling on successive occasions. We research a new sample size determination method additionally using change of population CV. We compare the proposed method with existing ones in various simulation settings.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.95-102
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• 2017

Gamma generalized linear models have received less attention than Poisson and binomial generalized linear models. Therefore, many old-established statistical techniques are still used in gamma generalized linear models. In particular, existing literature and textbooks still use approximate estimates for the dispersion parameter. In this paper we study the efficiency of various dispersion parameter estimators in gamma generalized linear models and perform numerical simulations. Numerical studies show that the maximum likelihood estimator and Cox-Reid adjusted maximum likelihood estimator are recommended and that approximate estimates should be avoided in practice.

• Journal of IKEEE
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• v.21 no.1
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• pp.96-99
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• 2017

In this study the influence of the random dopant fluctuation (RDF) depending on the halo and LDD implantations for the metal-oxide-semiconductor field effect transistor is investigated through the 3D atomistic device simulation. For accuracy in calculation, the kinetic monte carlo method that models individual impurity atoms and defects in the device was applied to the atomistic simulation. It is found that halo implantation has the greater influence on RDF effects than LDD implantation; three-standard deviation of $V_{TH}$ and $I_{ON}$ induced by halo implantation is about 6.45 times and 2.46 times those of LDD implantation. The distributions of $V_{TH}$ and $I_{ON}$ are also displayed in the histograms with normal distribution curves.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.733-749
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• 2018

Sample selection arises as a result of the partial observability of the outcome of interest in a study. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. Recently sample selection models for binomial and Poisson response variables have been proposed. Based on the theory of symmetry-modulated distribution, we extend these to a model for overdispersed count data. This type of data with no sample selection is often modeled using negative binomial distribution. Hence we propose a sample selection model for overdispersed count data using the negative binomial distribution. A real data application is employed. Simulation studies reveal that our estimation method based on profile log-likelihood is stable.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.41-52
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• 1993

In this paper we study numerical behavior of the adjustments for the variances of the pearson and deviance type dispersion statistics in two overdispersed mixture models; negative binomial and beta-binomial distribution. They are important families of an extended quasi-likelihood model which is very useful for the joint modelling of mean and dispersion. Comparisons are done for two types of dispersion statistics for various mean and dispersion parameters by simulation studies.