• 한국수자원학회논문집
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• 제45권8호
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• pp.827-837
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• 2012

확률강우량은 수공구조물의 설계에 있어 중요한 역할을 하며 이러한 확률강우량의 산정은 일반적으로 일변량 빈도해석을 수행하고 최적의 확률분포형을 찾아냄으로써 계산된다. 하지만 일변량 빈도해석은 수행 시 지속기간이 제한적이라는 단점이 있으며 이를 보완하기 위해 본 연구에서는 이변량 빈도해석을 수행하였다. 다변량 모형인 copula 모형 중3가지의 분포형을 이용하여 5개 지점의 연최대강우사상에 대해 이 변량 빈도해석을 수행하였으며 확률변수로 강우량과 지속기간을 사용하였다. 주변분포형은 강우량에는 Gumbel (GUM), generalized logistic (GLO) 분포형, 지속기간에는 generalized extreme value (GEV), GUM, GLO 분포형이사용됐으며 copula 모형은Frank, Joe, Gumbel-Hougaard 모형을 이용하였다. 주변분포형의 매개변수는 확률가중모멘트법을 이용하여 추정하였으며, copula 모형의 매개변수는 준모수방법인 의사최우도법을 사용하여 구하였다. 이를 통해 얻어진 확률강우량을 주변분포형과 copula 모형을바꾸어가며 비교하였다. 그 결과, 주변분포형의 종류에 따른 변화에서는 지속기간의 분포형에는 크게 영향을 받지 않는 것으로 나타났다. 강우량의 분포형에 따라서는 조금씩 차이가 났으며 강우량의 분포형이 GUM일 경우, GLO일 때에 비해 재현기간이 증가할수록 확률강우량이 증가하는 경향이 두드러졌다. Copula 모형별로 비교해보았을 때, Joe, Gumbel-Hougaard 모형은 비슷한 경향을 나타내었으며 Frank 모형은 재현기간의 증가에 따른 확률강우량의 증가가 강하게 나타냈다.

• Journal of Trauma and Injury
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• 제35권1호
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• pp.25-33
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• 2022

Purpose: The scoring system for traumatic liver injury (SSTLI) was developed in 2015 to predict mortality in patients with polytraumatic liver injury. This study aimed to validate the SSTLI as a prognostic factor in patients with polytrauma and liver injury through a generalized estimating equation analysis. Methods: The medical records of 521 patients with traumatic liver injury from January 2015 to December 2019 were reviewed. The primary outcome variable was in-hospital mortality. All the risk factors were analyzed using multivariate logistic regression analysis. The SSTLI has five clinical measures (age, Injury Severity Score, serum total bilirubin level, prothrombin time, and creatinine level) chosen based on their predictive power. Each measure is scored as 0-1 (age and Injury Severity Score) or 0-3 (serum total bilirubin level, prothrombin time, and creatinine level). The SSTLI score corresponds to the total points for each item (0-11 points). Results: The areas under the curve of the SSTLI to predict mortality on post-traumatic days 0, 1, 3, and 5 were 0.736, 0.783, 0.830, and 0.824, respectively. A very good to excellent positive correlation was observed between the probability of mortality and the SSTLI score (γ=0.997, P<0.001). A value of 5 points was used as the threshold to distinguish low-risk (<5) from high-risk (≥5) patients. Multivariate analysis using the generalized estimating equation in the logistic regression model indicated that the SSTLI score was an independent predictor of mortality (odds ratio, 1.027; 95% confidence interval, 1.018-1.036; P<0.001). Conclusions: The SSTLI was verified to predict mortality in patients with polytrauma and liver injury. A score of ≥5 on the SSTLI indicated a high-risk of post-traumatic mortality.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.183-183
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• 2015

수공구조물 설계 시 중요한 요소 중 하나인 확률강우량은 일반적으로 고정지속기간별 강우량에 대하여 일변량 빈도해석을 수행하고 가장 적절한 분포형을 선택하는 지점빈도해석의 과정을 거친다. 그러나 일변량 빈도해석을 수행하기 위해서는 지속시간을 고정하고 강우량의 변화로만 해석해야 단점이 있으며 이를 보완하기 위해 본 연구에서는 다변량 확률모형인 copula 모형을 이용하여 이변량 빈도해석을 수행하였다. 확률변수로는 강우량과 지속기간(hr)을 사용하였고, 주변분포형으로 강수량 - Gumbel (GUM), generalized logistic (GLO) 분포형, 지속기간(hr) - generalized extreme value (GEV), GUM, GLO 분포형을 사용하였으며, copula 모형은 Gumbel-Hougaard 모형을 이용하였다. 주변분포형의 매개변수는 일반적으로 가장 많이 사용하는 확률가중모멘트법을 이용하여 추정하였으며, copula 모형의 매개변수는 maximum pseudolikelihood(MPL) 방법을 사용하였다. 이를 통해 얻어진 이변량 빈도해석의 확률강우량 결과와 기존 지점빈도해석의 결과를 비교하였다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.179-182
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• 2003

This study describes a new graphical method for assessing and characterizing effect modification by a matching covariate in matched case-control studies. This method to understand effect modification is based on a semiparametric model using a varying coefficient model. The method allows for nonparametric relationships between effect modification and other covariates, or can be useful in suggesting parametric models. This method can be applied to examining effect modification by any ordered categorical or continuous covariates for which cases have been matched with controls. The method applies to effect modification when causality might be reasonably assumed. An example from veterinary medicine is used to demonstrate our approach. The simulation results show that this method, when based on linear, quadratic and nonparametric effect modification, can be more powerful than both a parametric multiplicative model fit and a fully nonparametric generalized additive model fit.

• 해양환경안전학회지
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• 제21권3호
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• pp.253-258
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• 2015

The purpose of this study is to find suitable probability distribution function of complex distribution data like multimodal. Normal distribution is broadly used to assume probability distribution function. However, complex distribution data like multimodal are very hard to be estimated by using normal distribution function only, and there might be errors when other distribution functions including normal distribution function are used. In this study, we experimented to find fit probability distribution function in multimodal area, by using AIS(Automatic Identification System) observation data gathered in Mokpo port for a year of 2013. By using chi-squared statistic, gaussian mixture model(GMM) is the fittest model rather than other distribution functions, such as extreme value, generalized extreme value, logistic, and normal distribution. GMM was found to the fit model regard to multimodal data of maritime traffic flow distribution. Probability density function for collision probability and traffic flow distribution will be calculated much precisely in the future.

• Journal of Forest and Environmental Science
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• 제36권1호
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• pp.7-16
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• 2020

Tree size distribution modelling is an integral part of forest management. Most distribution yield systems rely on some flexible probability models. In this study, a simple finite mixture of two components two-parameter Weibull distribution was compared with complex four-parameter distributions in terms of their fitness to predict tree size distribution of teak (Tectona grandis Linn f) plantations. Also, a system of equation was developed using Seemingly Unrelated Regression wherein the size distributions of the stand were predicted. Generalized beta, Johnson's SB, Logit-Logistic and generalized Weibull distributions were the four-parameter distributions considered. The Kolmogorov-Smirnov test and negative log-likelihood value were used to assess the distributions. The results show that the simple finite mixture outperformed the four-parameter distributions especially in stands that are bimodal and heavily skewed. Twelve models were developed in the system of equation-one for predicting mean diameter, seven for predicting percentiles and four for predicting the parameters of the finite mixture distribution. Predictions from the system of equation are reasonable and compare well with observed distributions of the stand. This simplified mixture would allow for wider application in distribution modelling and can also be integrated as component model in stand density management diagram.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.15-27
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• 2018

Parametric method of flood frequency analysis involves fitting of a probability distribution to observed flood data. When record length at a given site is relatively shorter and hard to apply the asymptotic theory, an alternative distribution to the generalized extreme value (GEV) distribution is often used. In this study, we consider the beta-P distribution (BPD) as an alternative to the GEV and other well-known distributions for modeling extreme events of small or moderate samples as well as highly skewed or heavy tailed data. The L-moments ratio diagram shows that special cases of the BPD include the generalized logistic, three-parameter log-normal, and GEV distributions. To estimate the parameters in the distribution, the method of moments, L-moments, and maximum likelihood estimation methods are considered. A Monte-Carlo study is then conducted to compare these three estimation methods. Our result suggests that the L-moments estimator works better than the other estimators for this model of small or moderate samples. Two applications to the annual maximum stream flow of Colorado and the rainfall data from cloud seeding experiments in Southern Florida are reported to show the usefulness of the BPD for modeling hydrologic events. In these examples, BPD turns out to work better than $beta-{\kappa}$, Gumbel, and GEV distributions.

• 응용통계연구
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• 제23권5호
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• pp.891-908
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• 2010

근래 소지역 추정(small area estimation)에 관한 연구는 비교적 활발하게 이루어진 편인데 비해, 우리나라의 국가통계 작성에 실제 활용된 사례는 거의 없는 실정이다. 이는 소지역 추정이 갖는 많은 장점에도 불구하고 공식통계 활용 여부를 판단하기가 그만큼 어렵기 때문이다. 본 연구는 소지역 추정방법에 의해 우리나라 시군구 실업자 통계를 생산하는 방법을 모색하고자 한다. 시군구 실업자수 추정은 로지스틱 선형혼합모형에 의한 EBLUP 타입(EBLUP-type) 추정량을 사용하였다. 실제자료분석과 모의실험 결과에 대해 다양한 평가 방법을 적용하고, 추정량의 특성을 비교 분석하였다. 그 결과 본 연구에서 적용한 로지스틱 선형혼합모형 기반 EBLUP 타입 추정량은 우리나라 시군구 실업자수 추정에 활용 가능성이 높은 것으로 평가되었다.

• 지역사회간호학회지
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• 제20권4호
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• pp.503-512
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• 2009

Purpose: This study was to investigate the factors influencing hotel workers' health practice. Methods: This study was based on the partial PRECEDE model. The subjects of this study were 261 servers sampled at random from a hotel located in Seoul. For the statistical analysis of collected data, descriptive statistics, t-test, ANOVA and logistic regression were performed with the SAS (Version. 8.01) program. Results: There were statistically significant primary factors influencing different parts of health practice. That is, regular exercise was influenced by gender, age and marital status, diet habit was by marital status, and type of working, prohibition of smoking was by gender, age and type of employment, and drinking by gender and job stress. Conclusion: This study has a limitation in generalized application to hotels in this country because it is a cross-sectional examination about the factors affecting health practice in the employees of a hotel. Further study is needed with various and broad variables that promote health practice and contributed to the development of health promotion programs.

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.713-722
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• 2009

The estimation of odds ratio and corresponding confidence intervals for case-control data have been done by traditional generalized linear models which assumed that the logarithm of odds ratio is linearly related to risk factors. We adapt a lower-dimensional approximation of Gu and Kim (2002) to provide a faster computation in nonparametric method for the estimation of odds ratio by allowing flexibility of the estimating function and its Bayesian confidence interval under the Bayes model for the lower-dimensional approximations. Simulation studies showed that taking larger samples with the lower-dimensional approximations help to improve the smoothing spline estimates of odds ratio in this settings. The proposed method can be used to analyze case-control data in medical studies.