• 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.30 no.4
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• pp.529-538
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• 2017

The Ghosh and Kim zero-altered distribution model is used to analyze count data that have too many or too few zeros. The dispersion type parameter ${\delta}$ in the zero-altered distribution model consists of mean, variance and zero probability and has two forms depending on the relation between ${\mu}$ and ${\sigma}^2$. We derived the influence function on ${\delta}$ when ${\sigma}^2{\geq}{\mu}$. To show the validity of the influence function, we used the Census data on the number of births of married women in Korea to compare the estimated changes in ${\delta}$ using this function with those obtained using the direct deletion method. The result proved that the obtained influence function is very accurate in estimating changes in ${\delta}$ when an observation is deleted.

• 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.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.15-22
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• 2003

In general, the linear regression model has been used to estimate trip generation in the travel demand forecasting procedure. However, the model suffers from several methodological limitations. First, trips as a dependent variable with non-negative integer show discrete distribution but the model assumes that the dependent variable is continuously distributed between -$\infty$ and +$\infty$. Second, the model may produce negative estimates. Third, even if estimated trips are within the valid range, the model offers only forecasted trips without discrete probability distribution of them. To overcome these limitations, a poisson model with a assumption of equidispersion has frequently been used to analyze count data such as trip frequencies. However, if the variance of data is greater than the mean. the poisson model tends to underestimate errors, resulting in unreliable estimates. Using overdispersion test, this study proved that the poisson model is not appropriate and by using Vuong test, zero inflated negative binomial model is optimal. Model reliability was checked by likelihood test and the accuracy of model by Theil inequality coefficient as well. Finally, marginal effect of the change of socio-demographic characteristics of households on trips was analyzed.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.77-81
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• 2019

식생전이에 따른 기간분리는 해외 및 국내 논문에서 식생영향을 받지 않는 곡선식과의 차이를 검토하고, 측정성과별 분류를 통해 곡선식을 개발하는 방법이 제시하고 있으나 적용기간 설정에 대한 구체적인 방법이 없다. 또한 상하류 비교를 통해 기간분리의 적정성을 검토할 수 있다고 명시되어 있으나 비교 관측소가 없는 경우 검토에 어려움이 있다. 본 연구는 2017년 수위-유량관계곡선식의 적용시간 결정에서 현장 식생모니터링 자료와 함께 연간 수온변화를 참고 자료로 활용하였다. 연간 수온자료를 산정하기 위해 측정된 수온과 해당 기온에 대한 상관관계식을 개발하였다. 또한 산포가 발생하는 측정성과는 생성된 연간 수온자료를 참고하여 전이여부를 판단하였다. 2018년은 2017년의 계절별 수온변화 자료 및 수집된 주요 수중식생의 생활사를 참고하여 연간 측정계획에 활용하였으며, 수위-유량관계곡선식 개발 시 동일한 방법을 사용하였다. 2017년은 가뭄으로 인해 수중식생에 의한 전이가 활발했던 기간으로 측정성과에 대해 식생영향 최고 기간과 최대 기간의 동일수위에 대해서 유량을 비교한 결과 $0.003m^3/s{\sim}1.099m^3/s$의 범위를 보였다. 유출량 비교의 경우 기간분리를 적용하지 않았을 경우 약 40.1% 과대 산정되었다. 2018년은 잦은 강우로 인해 수중식생의 이탈 및 전이 영향수위의 발생빈도가 비교적 적어 동 수위 유량을 비교한 결과 $0.19m^3/s{\sim}0.49m^3/s$의 범위를 보였다. 연 유출량을 비교한 결과, 기간분리를 고려하지 않은 경우가 약 39.6%로 과소 산정되었다. 따라서 본 연구사례를 통해 식생에 의한 기간분리가 발생하는 지점에 대해서 비교적 합리적인 측정주기 계획을 위한 확립 근거와 기간분리 적용기간에 대한 합리적인 자료로 활용 될 수 있을 것이다.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.263-274
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• 2016

In this study, we compared the Precipitable Water Vapor (PWV) data derived from the radiosonde observation at Sokcho observatory and the PWV data at Sokcho Global Navigation Satellite System (GNSS) observatory provided by Korea Astronomy and Space Science Institute, for the summer of 2007~2014, and analyzed the radiosonde diurnal and rainfall-dependent bias according to radiosonde sensor types. In the scatter diagram of the daytime and nighttime radiosonde PWV data and GNSS PWV data, dry bias was found in the daytime radiosonde observation as known in the previous study and dry bias of RSG-20A sensor was larger than other sensors. Overall, the tendency that the wet bias of the radiosonde PWV increased as GNSS PWV decreased and the dry bias of the radiosonde PWV increased as GNSS PWV increased. The quantitative analysis of the bias and error of the radiosonde PWV data showed that the mean bias decreased in the nighttime except for 2007, 2008 summer. In comparison for summer according to the presence or absence of rainfall, RS92-SGP sensor showed the highest quality.

• Korean Journal of Ecology and Environment
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• v.42 no.2
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• pp.200-211
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• 2009

A mathematical modeling program called Soil and Water Assessment Tool (SWAT) developed by USDA was applied to Kyongan stream watershed. It was run under BASINS (Better Assessment Science for Integrating point and Non-point Sources) program, and the model was calibrated and validated using KTMDL monitoring data of 2004${\sim}$2008. The model efficiency of flow ranged from very good to fair in comparison between simulated and observed data and it was good in the water quality parameters like flow range. The model reliability and performance were within the expectation considering complexity of the watershed and pollutant sources. The results of pollutant loads estimation as yearly (2004${\sim}$2008), pollutant loadings from 2006 were higher than rest of year caused by high precipitation and flow. Average non-point source (NPS) pollution rates were 30.4%, 45.3%, 28.1% for SS, TN and TP respectably. The NPS pollutant loading for SS, TN and TP during the monsoon rainy season (June to September) was about 61.8${\sim}$88.7% of total NPS pollutant loading, and flow volume was also in a similar range. SS concentration depended on precipitation and pollution loading patterns, but TN and TP concentration was not necessarily high during the rainy season, and showed a decreasing trend with increasing water flow. SWAT based on BASINS was applied to the Kyongan stream watershed successfully without difficulty, and it was found that the model could be used conveniently to assess watershed characteristics and to estimate pollutant loading including point and non-point sources in watershed scale.