• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2009.11a
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• pp.339-343
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• 2009

The aim of the study was to investigate the statistical distribution of muzzle velocity of 155mm propelling charge K676 which is for the use of K9, a korean 155mm self-propelled artillery. A plenty of muzzle velocity data were collected from lot assessment test of propelling charge. The muzzle velocity of each test round is compensated by reference round. In the present work, the detailed statistical analysis of the muzzle velocity data is carried out using probability models including normal, Weibull 2-parameter and Weibull 3-parameter distributions. The results of goodness of fit test showed that the normal distribution described more appropriately the experimentally measured muzzle velocity data and the Weibull distribution is also applicable. The coefficient of variation showed that the mass production capability of each propelling charge lot has been maintained.

• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.917-928
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• 2020

The purpose of this study is to find the appropriate probability distribution representing the size distribution of suspended cohesive sediment. Based on goodness-of-fit test for a significance level of 5% using the Kolmogorov-Smirnov test, it is found that the floc size distributions measured in laboratory experiment and field study show different results. In the case of sample data collected from field experiments, the Gamma distribution is the best fitting form. In the case of laboratory experiment results, the sample data shows the positively-skewed distribution and the GEV distribution is the best fitted. The lognormal distribution, which is generally assumed to be a floc size distribution, is not suitable for both field and laboratory results. By using 3-parameter lognormal distribution, it is shown that similar size distribution with floc size distribution can be simulated.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.579-590
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• 2017

The multivariate empirical distribution function could be defined when its distribution function can be estimated. It is known that bivariate empirical distribution functions could be visualized by using Step plot and Quantile plot. In this paper, the multivariate empirical distribution plot is proposed to represent the multivariate empirical distribution function on the unit square. Based on many kinds of empirical distribution plots corresponding to various multivariate normal distributions and other specific distributions, it is found that the empirical distribution plot also depends sensitively on its distribution function and correlation coefficients. Hence, we could suggest five goodness-of-fit test statistics. These critical values are obtained by Monte Carlo simulation. We explore that these critical values are not much different from those in text books. Therefore, we may conclude that the proposed test statistics in this work would be used with known critical values with ease.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1217-1222
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• 2011

In this paper, we show that any circular density can be closely approximated by an exponential family of distributions. Therefore we propose an exponential family of distributions as a new family of circular distributions, which is absolutely suitable to model any shape of circular distributions. In this family of circular distributions, the trigonometric moments are found to be the uniformly minimum variance unbiased estimators (UMVUEs) of the parameters of distribution. Simulation result and goodness of fit test using an asymmetric real data set show usefulness of the novel circular distribution.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.338-343
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• 1998

This study is to select appropriate probability distributions for ten days evaporation data for the purpose of representing statistical characteristics of real evaporation data in Korea. Nine probability distribution functions were assumed to be underlying distributions for ten days evaporation data of 20 stations with the duration of 20 years. The parameter of each probability distribution function were estimated by the maximum likelihood approach, and appropriate probability distributions were selected from the goodness of fit test. Log Pearson type III model was selected as an appropriate probability distribution for ten days evaporation data in Korea.

• Journal of Korean Society for Quality Management
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• v.25 no.1
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• pp.44-55
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• 1997

In order to determine procedures for a, pp.opriate model selection of technological growth curves, numerous time series that were representative of growth behavior were collected according to data characteristics. Three different growth curve models were fitted onto data sets in an attempt to determine which growth curve models achieved the best forecasts for types of growth data. The analysis of the results gives rise to an a, pp.oach for selecting a, pp.opriate growth curve models for a given set of data, prior to fitting the models, based on the characteristics of the goodness of fit test.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.911-920
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• 2004

We try receiver operating characteristic(ROC) curves by neural networks of logistic function. The models are shown to arise from model classification for normal (diseased) and abnormal (nondiseased) groups in medical research. A few goodness-of-fit test statistics using normality curves are discussed and the performances using neural networks of logistic function are conducted.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.1
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• pp.12-18
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• 1988

It is found that the estimation pdf of the real intraframe DPCM prediction error by $x^3$ goodness-of-fit test for the images is nearer gamma distribution that laplacian. Also the new pdf estimation method by NMAE is proposed and applied to the pdf adaptive DPCM system.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.119-129
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• 2003

We propose a new logistic regression model of normality curves for normal(diseased) and abnormal(nondiseased) classifications by neural networks in data mining. The fitted logistic regression lines are estimated, interpreted and plotted by the neural network technique. A few goodness-of-fit test statistics for normality are discussed and the performances by the fitted logistic regression lines are conducted.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.793-798
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• 2012

In this paper, we propose regression methods based on the likelihood function. We assume Arnold-Beaver Skew Normal(ABSN) errors in a simple linear regression model. It was shown that the novel method performs better with an asymmetric data set compared to the usual regression model with the Gaussian errors. The utility of a novel method is demonstrated through simulation and real data sets.