• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.279-289
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• 2010

In this study, we propose a simple statistical model for determining the admission or discharge of 668 patients with a chief complaint of dyspnea. For this, we use 11 explanatory variables which are chosen to be important by clinical experts among 55 variables. As a modification process, we determine the discharge interval of each variable by the kernel density functions of the admitted and discharged patients. We then choose the optimal model for determining the discharge of patients based on the number of explanatory variables belonging to the corresponding discharge intervals. Since the numbers of the admitted and discharged patients are not balanced, we use, as the criteria for selecting the optimal model, the arithmetic mean of sensitivity and specificity and the harmonic mean of sensitivity and precision. The selected optimal model predicts the discharge if 7 or more explanatory variables belong to the corresponding discharge intervals.

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.43-54
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• 2010

In recent years there have been studies on tour based approaches for freight travel demand modelling. The purpose of this paper is to analyze tour type choice behavior of commercial vehicles which are divided into round trips and chained tours. The methods of the study are based on the decision tree and the logit model. The results indicates that the explanation variables for classifying tour types of commercial vehicles are loading factor, average goods quantity, and total goods quantity. The results of the decision tree method are similar to those of logit model. In addition, the explanation variables for tour type classification of small trucks are not different from those for medium trucks', implying that the most important factor on the vehicle tour planning is how to load goods such as shipment size and total quantity.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.69-77
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• 2004

본 논문에서는 한방병원에서 사상체질분류검사설문지를 이용하여 사상체질을 진단할 때 진단의 정확도를 향상시키기 위한 사상체질분류함수를 개발하기 위하여 데이터마이닝에서의 판별분석모형을 이용한다. 데이터 정제 과정에서 양질의 데이터를 확보하기 위한 기준은 상반되는 설문의 응답 패턴과 체질별 설문의 응답 비율을 이용하며, 변수선택의 기준은 도수분석의 비율차이검정과 선형판별함수의 계수를 이용한다.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.61-68
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• 2004

본 논문에서는 한방병원에서 사상체질분류검사설문지를 이용하여 사상체질을 진단할 때 진단의 정확도를 향상시키기 위한 사상체질분류함수를 개발하기 위하여 데이터마이닝에서의 판별분석모형을 이용한다. 데이터 정제 과정에서 불성실한 응답자를 제거시키기 위한 기준은 상반되는 설문의 응답 패턴과 체질별 설문의 응답 비율을 이용하며, 변수선택의 기준은 상관분석의 크론박 알파 계수와 선형판별함수의 계수를 이용한다.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.281-301
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• 2004

In this paper we consider the change point problem in a sequence of univariate normal observations. We want to know whether there is any change point or not. In case a change point exists, we will identify its change type. Namely, it can be a mean change, a variance change, or both the mean and variance change. The intrinsic Bayes factors of Berger and Pericchi (1996, 1998) are used to find the type of optimal change model. The Gibbs sampling including the Metropolis-Hastings algorithm is used to estimate all the parameters in the change model. These methods are checked via simulation and applied to the winter average temperature data in Seoul.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.57-71
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• 2002

We introduce three types of exponential survival models, such as simple model, change-point model and finite mixture model in this paper. Among these models, in order to choose the best model, the model choice method is proposed using Gelfand and Ghosh(1998)'s idea. Then to avoid the computational difficulties, data augmentation method (Tanner and Wong, 1987) and Gibbs sampler (Gelfand and Smith, 1990) are employed. Our methodology is applied to both simulated data and Stangl (1991)'s On-impramint Hydrochloride data.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.489-498
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• 2004

In this paper, a new form of linear models referred to as generalized weighted linear models is proposed. The proposed models assume that the relationship between the response variable and explanatory variables can be modelled by a distribution function of the response mean and a weighted linear combination of distribution functions of covariates. This form addresses a structural problem of the link function in the generalized linear models in which the parameter space may not be consistent with the space derived from linear predictors. The maximum likelihood estimation with Lagrange's undetermined multipliers is used to estimate the parameters and resampling method is applied to compute confidence intervals and to test hypotheses.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.564-564
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• 2016

개념적 수문 모형은 탱크의 개수, 탱크 간 관계구조, 그리고 저류량과 유출량 간 선형/비선형 관계 정의 방식 등에 따라 다양한 형태로 개발되어왔으며, 각 모형마다 매개변수 수 및 입력 자료가 상이하다. 모형의 매개변수가 많아지면 결과가 좋게 나타날 수 있으나, 늘어난 매개변수에 대해 물리적 의미를 부여하고 해석하기가 쉽지 않다. 단순한 모형은 보정이 용이하고 그 특성상 실무에서 널리 이용되고 있으나, 물순환 구조가 복잡한 유역에 대해서는 적용성이 떨어질 수 있다. 하지만 매개변수의 수가 많은 모형이 적은 모형에 비해 항상 결과가 좋은 것은 아니다. 복잡한 모형은 부족한 안정성에 의해 보정 기간에서는 결과가 좋았으나, 검정 기간 대해 결과가 안 좋을 수도 있으며 이에 대한 평가가 필요하다. 본 연구에서는 국내에서 주로 이용되는 개념적 모형을 대상으로 모형의 복잡성과의 정확도의 관계를 비교 평가하고자 한다. 대상 모형으로는 수정 3단 Tank 모형, Im's Tank 모형, Two-Parametric Hyperbolic Model (TPHM), 그리고 Daily Watershed Streamflow Model (DAWAST)을 선정하였고, 대상유역으로는 이동저수지 상류에 위치한 2개 유역을 선정하였다. 모형 간 비교를 위한 정량적 통계적 지표로 $R^2$, Nash-Sutcliffe efficiency (NSE), root mean square error-observations standard deviation ratio (RSR), 그리고 percent bias (PBIAS)를 이용하였다. 본 연구 결과는 개념적 수문 모형에 대한 이해를 증진하고, 장기유출 해석을 위한 수문 모형의 선택 시 모형의 복잡도 및 정확도의 관점에서 도움을 줄 수 있는 기초자료로 이용될 수 있을 것이다.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.881-891
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• 2014

Graphical models can be used as an intuitive tool for modeling a complex stochastic system with a large number of variables related each other because the conditional independence between random variables can be visualized as a network. Graphical least absolute shrinkage and selection operator (LASSO) is considered to be effective in avoiding overfitting in the estimation of Gaussian graphical models for high dimensional data. In this paper, we consider the model selection problem in graphical LASSO. Particularly, we compare various model selection criteria via simulations and analyze a real financial data set.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.27-39
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• 2016

This paper considers variable selection in the sparse vector autoregressive (sVAR) model where sparsity comes from setting small coefficients to exact zeros. In the estimation perspective, Davis et al. (2015) showed that the lasso type of regularization method is successful because it provides a simultaneous variable selection and parameter estimation even for time series data. However, their simulations study reports that the regular lasso overestimates the number of non-zero coefficients, hence its finite sample performance needs improvements. In this article, we show that the adaptive lasso significantly improves the performance where the adaptive lasso finds the sparsity patterns superior to the regular lasso. Some tuning parameter selections in the adaptive lasso are also discussed from the simulations study.