• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.689-700
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• 2016

We study estimation and inference of the joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. For the estimation of marginal models we consider a class of time-varying transformation models and combine the two marginal models using nonparametric empirical copulas. Regression parameters in the transformation model can be obtained as the solution of estimating equations and our models and estimation method can be applied in many situations where the conditional mean-based models are not good enough. Nonparametric copulas combined with time-varying transformation models may allow quite flexible modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1057-1061
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• 2004

본 연구에서는 준분포형 지표수 유출모형인 SWAT과 3차원 지하수 유동모형인 MODFLOW의 완전연동형 결합모형을 독자적으로 개발했다. SWAT의 지하수 모형성분은 집중형이므로 분포형 매개변수와 변화하는 양수량, 지하수위의 변화등을 고려하지 못하며 MODFLOW모형은 주요 입력자요인 함양량의 정확한 산정이 어렵다. 두 프로그램의 연결작업은 지하수 함양량의 전달과정과 하천네트워크-대수층간의 상호작용을 고려하여 완성하였으며 경안천유역의 오산천 소유역에 모형을 시험구동을 수행했다. 시험구동결과 결합모형은 수문모형 혹은 지하수 모형만으로는 해결되지 않는 하천-대수층간의 경계유량을 고려한 유출해석이 가늠해짐으로써 유역내 지하수 유출량 및 총 유출량의 신뢰성이 증대될 것으로 기대된다.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.53-58
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• 2015

본 연구는 한국어 정보처리를 위한 형태소 연구 중 선어말어미 분석과 합성을 위한 처리 모형을 제안한다. 이를 위해 (1) 어미를 정의하고 선정한 뒤 (2) 낱말 패러다임 형태 이론에 기반하여 동사 어간을 그 특징에 따라 적절하게 분류한다. (3) 또한 형태소 결합을 위해 필요한 조작들을 기술하고 (4) 마지막으로 어미의 결합 순서와 결합 제약을 만족시킬 규칙을 만들어 제시함으로써 각 조작과 규칙을 이용하여 기계 분석을 하기 위한 프로그램 모형을 내놓는다.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1235-1243
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• 2015

Increased data volume in the ICT area has increased the importance of forecasting accuracy for internet traffic. Forecasting results may have paper plans for traffic management and control. In this paper, we propose combined forecasts based on several time series models such as Seasonal ARIMA and Taylor's adjusted Holt-Winters and Fractional ARIMA(FARIMA). In combined forecasting methods, we use simple-combined method, MSE based method (Armstrong, 2001), Ordinary Least Squares (OLS) method and Equality Restricted Least Squares (ERLS) method. The results show that the Seasonal ARIMA model outperforms in 3 hours ahead forecasts and that combined forecasts outperform in longer periods.

• Journal of Korea Water Resources Association
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• v.37 no.7
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• pp.589-598
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• 2004

From the continuous long-term rainfall-runoff standpoint, the urbanization within a watershed causes land use change due to the increase in impervious areas, the addition of manmade structures, and the changes in river environment. Therefore, rainfall-runoff characteristics changes drastically after the urbanization. Due to these reasons, there exists the demand for rainfall-runoff simulation model that can quantitatively evaluate the components of hydrologic cycle including surface runoff, river flow, and groundwater by considering urban watershed characteristics as well as natural runoff characteristics. In this study, continuous long-term rainfall-runoff simulation model SWAT-SWMM is developed by coupling semi-distributed continuous long-term rainfall-runoff simulation model SWAT with RUNOFF block of SWMM, which is frequently used in the runoff analysis of urban areas in order to consider urban watershed as well as natural watershed. The coupling of SWAT and SWMM is described with emphasis on the coupling scheme, model limitations, and the schematics of coupled model.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.123-136
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• 2020

It is common to encounter correlated multiple outcomes measured on the same subject in various research fields. In developmental toxicity studies, presence of malformed pups and fetal weight are measured on the pregnant dams exposed to different levels of a toxic substance. Joint analysis of such two outcomes can result in more efficient inferences than separate models for each outcome. Most methods for joint modeling assume a normal distribution as random effects. However, in developmental toxicity studies, the response distributions may change irregularly in location and shape as the level of toxic substance changes, which may not be captured by a normal random effects model. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint model for binary and continuous outcomes. In our model, we incorporate a skewed logit model for the binary outcome to allow the response distributions to have flexibly in both symmetric and asymmetric shapes on the toxic levels. We apply our proposed method to data from a developmental toxicity study of diethylhexyl phthalate.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.297-309
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• 2024

This paper evaluates the performance of the neural network autoregressive model combined with an exponential smoothing model, called the NNARX+ETS model. The combined model utilizes the components of ETS as exogenous variables for NNARX, to forecast time series data using artificial neural networks. The main idea is to enhance the performance of NNAR using only lags of the original time series data, by combining traditional time series analysis methods with the neural networks through NNARX. We employ two real data for performance evaluation and compare the NNARX+ETS with NNAR and traditional time series analysis methods such as ETS and ARIMA (autoregressive integrated moving average) models.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.447-461
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• 2018

Domestic film industry sales are increasing every year. Theaters are the primary sales channels for movies and the number of audiences using the theater affects additional selling rights. Therefore, the number of audiences using the theater is an important factor directly linked to movie industry sales. In this paper we consider a hybrid model that combines a multiple linear regression model and the Bass model to predict the audience numbers for a specific day. By combining the two models, the predictive value of the regression analysis was corrected to that of the Bass model. In the analysis, three films with different release dates were used. All subset regression method is used to generate all possible combinations and 5-fold cross validation to estimate the model 5 times. In this case, the predicted value is obtained from the model with the smallest root mean square error and then combined with the predicted value of the Bass model to obtain the final predicted value. With the existence of past data, it was confirmed that the weight of the Bass model increases and the compensation is added to the predicted value.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.4
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• pp.270-278
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• 2010

Applicability of the MIKE21 model as a real time coupled tide-surge model had been examined at the previous study. In this study, another applicability of the model as an inundation model is also examined. Prior to real cases, effect of artificial structures on the inundation is analyzed. The results show that inundation depth is not altered, while inundation area is lessened as a result of decreased inundation speed. Comparative study between the coupled model and an uncoupled storm surge model is also carried out at the Masan coastal zone, which shows the coupled model is considered to be plausible at the time to maximum inundation, while both models show similar results at the inundation area and inundation depth.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1459-1473
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• 2016

Both longitudinal data and survival data are collected simultaneously in longitudinal data which are observed throughout the passage of time. In this case, the effect of the independent variable becomes biased (provided that sole use of longitudinal data analysis does not consider the relation between both data used) if the missing that occurred in the longitudinal data is non-ignorable because it is caused by a correlation with the survival data. A joint model of longitudinal data and survival data was studied as a solution for such problem in order to obtain an unbiased result by considering the survival model for the cause of missing. In this paper, a joint model of the longitudinal zero-inflated count data and survival data is studied by replacing the longitudinal part with zero-inflated count data. A hurdle model and proportional hazards model were used for each longitudinal zero inflated count data and survival data; in addition, both sub-models were linked based on the assumption that the random effect of sub-models follow the multivariate normal distribution. We used the EM algorithm for the maximum likelihood estimator of parameters and estimated standard errors of parameters were calculated using the profile likelihood method. In simulation, we observed a better performance of the joint model in bias and coverage probability compared to the separate model.