• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.205-220
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• 2009

For modeling skewed semicircular data, we derive new family of the exponential distributions. We extend it to the l-axial exponential distribution by a transformation for modeling any arc of arbitrary length. It is straightforward to generate samples from the f-axial exponential distribution. Asymptotic result reveals two things. The first is that linear exponential distribution can be used to approximate the l-axial exponential distribution. The second is that the l-axial exponential distribution has the asymptotic memoryless property though it doesn't have strict memoryless property. Some trigonometric moments are also derived in closed forms. Maximum likelihood estimation is adopted to estimate model parameters. Some hypotheses tests and confidence intervals are also developed. The Kolmogorov-Smirnov test is adopted for goodness of fit test of the l-axial exponential distribution. We finally obtain a bivariate version of two kinds of the l-axial exponential distributions.

• 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.

• Proceedings of the KIEE Conference
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• summer
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• pp.548-550
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• 2004

This paper suggests a methodology for calculation of photovoltaic(PV) generation system output using probability distribution function, PV way efficiency and PV system design Parameters. Long term irradiance recorded for every hour of the day for 11 years were used. For goodness-fit test, several distribution functions are tested by Kolmogorov- Smirnov(K-S) test. And the calculated generation output is compared with that of CMS(Centered Monitoring System), which can monitoring PV generation output of each PV generation site.

• International Journal of Reliability and Applications
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• v.14 no.1
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• pp.27-39
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• 2013

In this article, a new model based on the Rayleigh distribution is introduced. This model is useful and practical in physics, reliability, and life testing. The statistical and reliability properties of this model are presented, including moments, the hazard rate, the reversed hazard rate, and mean residual life functions, among others. In addition, it is shown that the distributions of the new model are ordered regarding the strongest likelihood ratio ordering. Four estimating methods, namely, method of moment, maximum likelihood method, Bayes estimation, and uniformly minimum variance unbiased, are used to estimate the parameters of this model. Simulation is used to calculate the estimates and to study their properties. Finally, the appropriateness of this model for real data sets is shown by using the chi-square goodness of fit test and the Kolmogorov-Smirnov statistic.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.4
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• pp.188-194
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• 2005

There are many factors that affect on the system output of Photovoltaic(PV) power generation; the variation of solar radiation, temperature, energy conversion efficiency of solar cell etc. This paper suggests a methodology for calculation of PV generation output using the probability distribution function of irradiance, PV array efficiency and revision factors of solar cell conversion efficiency. Long-term irradiance data recorded every hour of the day for 11 years were used. For goodness-fit test, several distribution (unctions are tested by Kolmogorov-Smirnov(K-S) method. The calculated generation output with or without revision factors of conversion efficiency is compared with that of CMS (Centered Monitoring System), which can monitor PV generation output of each PV generation site.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.6
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• pp.74-79
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• 2001

In this study, a failure mode analysis of CNC machining center is described. First, the system is classified through subsystems into components using part lists and drawings. The component failure rate and failure mode analysis are performed to identify the weak components of a machining center with reliability database. The failure probabilistic function of mechanical part is analyzed by Weibull distribution. The Kolmogorov-Smirnov test is also used to verify the goodness of fit.

• Journal of Wetlands Research
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• v.2 no.1
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• pp.49-58
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• 2000

The frequency analysis of annual maximum rainfall data and the derivation of probable rainfall intensity formula at Masan station are performed in this study. Based on the eight different rainfall duration data from 10 minutes to 24 hours, eight types of probability distribution (Gamma, Lognormal, Log-Pearson type III, GEV, Gumbel, Log-Gumbel, Weibull, and Wakeby distributions), three types of parameter estimation scheme (moment, maximum likelihood and probability weighted methods) and three types of goodness-of-fit test (${\chi}^2$, Kolmogorov-Smirnov and Cramer von Mises tests) were considered to find an appropriate probability distribution at Masan station. The Lognormal-2 distribution was selected and the probable rainfall intensity formula was derived by regression analysis. The derived formula can be used for estimating rainfall quantiles of the Masan vicinity areas with convenience and reliability in practice.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.1010-1013
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• 2000

In this study, a failure mode analysis of CNC machining center is described. At first, the system is classified through subsystems into components using part lists and drawings. The components failure rate and failure mode analysis are performed to identify the weak components of a machining center with reliability database. The failure probabilistic function of mechanical part is analyzed by Weibull distribution. The Kolmogorov-Smirnov test is also used to verify the goodness of fit.

• Journal of the Korea Concrete Institute
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• v.16 no.5 s.83
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• pp.581-589
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• 2004

This paper compares the fatigue behaviors of concretes subjected to flexural and split-tensional loadings, and proposes the fatigue reliability models based on experimental results and reliability analysis. The fatigue tests were performed for the specimens of $150 mm{\times}75 mm$ split tensional cylinders and $150 mm{\times}150 mm{\times}550 mm$ flexural beams under constant loadings at three levels (70, 80 and $90\%$) with 0.1 stress ratio, 20 Hz loading speed and sine wave. The reliability analysis on fatigue data was based on Weibull distribution of two-parameters. From fatigue test results, two criteria were proposed to reject the experimental fatigue data because of statistical variation of concrete fatigue data. Two parameters ($\alpha$and u) of Weibull distribution were obtained using graphical method, moment method and maximum likelihood method. The probability density function(P.D.F) and cumulative distribution function(C.D.F) of the Weibull distribution for fatigue life of pavement concrete were derived for various stress levels using parameters, $\alpha$ and u. The goodness-of-fit test by Kolmogorov-Smirnov test was acceptable at $5\%$ level of significance. Based on reliability analysis, a fatigue model for pavement concrete was proposed and compared from existing models.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5287-5294
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• 2016

Background: Breast cancer is a worldwide public health concern and is the most prevalent type of cancer in women in the United States. This study concerned the best fit of statistical probability models on the basis of survival times for nine state cancer registries: California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, and Washington. Materials and Methods: A probability random sampling method was applied to select and extract records of 2,000 breast cancer patients from the Surveillance Epidemiology and End Results (SEER) database for each of the nine state cancer registries used in this study. EasyFit software was utilized to identify the best probability models by using goodness of fit tests, and to estimate parameters for various statistical probability distributions that fit survival data. Results: Statistical analysis for the summary of statistics is reported for each of the states for the years 1973 to 2012. Kolmogorov-Smirnov, Anderson-Darling, and Chi-squared goodness of fit test values were used for survival data, the highest values of goodness of fit statistics being considered indicative of the best fit survival model for each state. Conclusions: It was found that California, Connecticut, Georgia, Iowa, New Mexico, and Washington followed the Burr probability distribution, while the Dagum probability distribution gave the best fit for Michigan and Utah, and Hawaii followed the Gamma probability distribution. These findings highlight differences between states through selected sociodemographic variables and also demonstrate probability modeling differences in breast cancer survival times. The results of this study can be used to guide healthcare providers and researchers for further investigations into social and environmental factors in order to reduce the occurrence of and mortality due to breast cancer.