• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.777-792
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• 1998

병원의 임상연구실험에서 종종 환자들의 치료에 따른 병세의 호전상태를 여러단계로 분류하여 상이한 치료방법에 대한 치료효과간의 차이론 알고자 하는 경우가 있다. 이와 같이 다중상태의 생존자료를 분석하기 위해서 본 논문에서는 semi-Markov 모형에 Cox 회귀모형을 적용하여 회귀계수와 기저생존함수를 추정하고 이를 바탕으로 반응확률함수를 추정하였다. 그리고 본 논문의 결과를 실제 임상실험에서 얻어진 자료에 적용하여 분석하였다.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1311-1327
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• 2016

We propose a multi-state model for analyzing semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the 'illness-death model', which composes three states, such as 'healthy', 'diseased', and 'dead'. The state of 'diseased' can be considered as an intermediate event. Two more states are added into the illness-death model to describe missing events caused by a loss of follow-up before the end of the study. One of them is a state of 'LTF', representing a lost-to-follow-up, and the other is an unobservable state that represents the intermediate event experienced after LTF occurred. Given covariates, we employ the Cox proportional hazards model with a normal frailty and construct a full likelihood to estimate transition intensities between states in the multi-state model. Marginalization of the full likelihood is completed using the adaptive Gaussian quadrature, and the optimal solution of the regression parameters is achieved through the iterative Newton-Raphson algorithm. Simulation studies are carried out to investigate the finite-sample performance of the proposed estimation procedure in terms of the empirical coverage probability of the true regression parameter. Our proposed method is also illustrated with the dataset adapted from Helmer et al. (2001).

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.581-594
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• 2016

We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.1
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• pp.73-80
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• 2018

In motorway traffic flow control, the conventional way based on real-time response has been changed into advanced way based on proactive response. Future traffic conditions over multiple time intervals are crucial input data for advanced motorway traffic flow control. It is necessary to overcome the uncertainty of the future state in order for forecasting multiple-period traffic volumes, as the number of uncertainty concurrently increase when the forecasting horizon expands. In this vein, multi-interval forecasting of traffic volumes requires a viable approach to conquer future uncertainties successfully. In this paper, a forecasting model is proposed which effectively addresses the uncertainties of future state based on the behaviors of temporal evolution of traffic volume states that intrinsically exits in the big past data. The model selects the past states from the big past data based on the state evolution of current traffic volumes, and then the selected past states are employed for estimating future states. The model was also designed to be suitable for data management systems in practice. Test results demonstrated that the model can effectively overcome the uncertainties over multiple time periods and can generate very reliable predictions in term of prediction accuracy. Hence, it is indicated that the model can be mounted and utilized on advanced data management systems.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.263-268
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• 1999

지능형 교육 시스템에서 학습자 모델은 학습자의 반응을 토대로 교수모듈과 전문가 모듈을 연계하여 새로운 학습자 모델을 제시하는 역할을 수행하고 있으며, 이는 성공적인 지능형 교육 시스템의 구현에 있어서 핵심적인 부분이다. 따라서 많은 대학교 및 연구소에서 그동안 학습자 모형에 관한 많은 연구가 이루어져오고 있다. 그러나 대부분의 연구는 단일 학습자 모형을 기반으로 두고 있으며, 이러한 단일 학습자 모형을 이용한 시스템들은 학습자의 지식 또는 학습자의 성향을 정확히 파악하기는 어려움을 갖고 있을 뿐만 아니라 다른 모듈과의 인터페이스 부분에서 중복된 많은 정보를 가지고 있다. 따라서 본 논문에서는 학습자의 지식을 정확하게 진단하고 각 모듈간의 중복된 정보를 보완할 수 있는 다중 학습자 모형을 개발하여 구현하였다. 또한 이러한 다중 학습자 모형을 최적으로 수행할 수 있도록 하기위하여 agent기법을 적용하였다. Agent를 이용한 다중 학습자 모형을 적용하여 구현한 시스템은 첫째, 단계적인 접근 방법으로 보다 정확한 학습자의 지식 진단이 가능하다. 둘째, 학습과정중 학습자의 심리 상태 및 학습자의 선호도 등 파악이 용이하다. 셋째, 교수모듈과 전문가 모듈과의 연계에 있어서 정보의 중복됨의 최소화 등의 장점을 제공한다.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.967-975
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• 2010

Since the least squares estimation is not appropriate when multicollinearity exists among the regressors of the linear regression model, the principal components regression is used to deal with the multicollinearity problem. This article suggests a new procedure for the selection of suitable principal components. The procedure is based on the condition index instead of the eigenvalue. The principal components corresponding to the indices are removed from the model if any condition indices are larger than the upper limit of the cutoff value. On the other hand, the corresponding principal components are included if any condition indices are smaller than the lower limit. The forward inclusion method is employed to select proper principal components if any condition indices are between the upper limit and the lower limit. The limits are obtained from the linear model which is constructed on the basis of the conjoint analysis. The procedure is evaluated by Monte Carlo simulation in terms of the mean square error of estimator. The simulation results indicate that the proposed procedure is superior to the existing methods.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.53-77
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• 1994

In a certain stochastic process, Cox's regression model is used to analyze multistate survival data. From this model, the regression parameter vectors, survival functions, and the probability of being in response function are estimated based on multistate Cox's partial likelihood and nonparametric likelihood methods. The asymptotic properties of these estimators are described informally through the counting process approach. An example is given to likelihood the results in this paper.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.514-514
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• 2015

홍수 예측의 개선에 있어 정확한 공간 토양수분 정보는 필수적이다. 위성관측을 활용한 토양수분관측이 이루어지고 있으나 실제적 토양수분 상태와 정량적 차이가 크므로 편이보정을 통한 정량적 개선과정이 요구되는 실정이다. 따라서, 본 연구에서는 위성에서 관측한 AMSR2 토양수분과 지상관측 토양수분자료 및 다중회귀모형를 이용하여 토양수분자료를 정량적로 개선하였다. 공간 해상도가 10 km인 AMSR2 토양수분을 1 km로 상세화한 우리나라 전역의 토양수분 자료와 수자원관리종합정보시스템(WAMIS)에서 제공하는 강우관측소 556개 지점에서 관측한 강우자료, 후처리한 MODIS LST 자료, 증발산량 및 식생지수를 사용하였다. 2012년 7월부터 2013년까지 기상청 농업기상관측관서에서 관측하는 지점 중 사용 가능한 6개 토양수분관측소 자료에 대해 토양군별회귀계수를 산정하였다. 토양군별 다중회귀모형을 이용하여 편이보정한 토양수분자료는 전반적으로 과소추정되는 AMSR2 토양수분의 단점을 개선하여 위성관측 토양수분자료의 활용성을 개선하였다(Fig. 1).

• Journal of the Korean Geotechnical Society
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• v.29 no.11
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• pp.99-106
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• 2013

In this study, the correlation of evaluation items and safety rating for 104 of large-scale slopes along the general national road was analyzed. And, we proposed the regression model to predict the safety rating using the multiple regressions analysis. As the result, it is shown that the evaluation items of slope angle, rainfall and groundwater have a low correlation with safety rating. Also, the regression model suggested by multiple regression analysis shows high predictive value, and it would be possible to apply if the evaluation items of excavation condition and groundwater (rainfall) are not clear.