• Journal of Korea Water Resources Association
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• v.38 no.6 s.155
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• pp.471-483
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• 2005

A monthly Ensemble Streamflow Prediction (ESP) system was developed by applying a daily rainfall-runoff model known as the Streamflow Synthesis and Reservoir Regulation (SSARR) model to the Han, Nakdong, and Seomjin River basins in Korea. This study first assesses the accuracy of the averaged monthly runoffs simulated by SSARR for the 3 basins and proposes some improvements. The study found that the SSARR modeling of the Han and Nakdong River basins tended to significantly underestimate the actual runoff levels and the modeling of the Seomjin River basinshowed a large error variance. However, by implementing optimal linear correction (OLC), the accuracy of the SSARR model was considerably improved in predicting averaged monthly runoffs of the Han and Nakdong River basins. This improvement was not seen in the modeling of the Seomjin River basin. In addition, the ESP system was applied to forecast probabilistic runoff forecasts one month in advance for the 3 river basins from 1998 to 2003. Considerably improvement was also achieved with OLC in probabilistic forecasting accuracy for the Han and Nakdong River basins, but not in that of the Seomjin River basin.

• The Korean Journal of Financial Management
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• v.8 no.1
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• pp.123-137
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• 1991

This paper developes a multiperiod trading model of securities price formation which extends the notion of market created risk introduced by Kraus and Smith [1989]. It is shown that stock price volalitility can depend on combinations of market parameters known to the market participants only imperfectly. Resulting portfolio rebalancing equilibria generate self-justifying price movements while market fundamental remain unchanged.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1479-1494
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• 2015

Since the Markowitz's mean-variance framework for portfolio analysis, the topic of portfolio optimization has been an important topic in finance. Traditional approaches focus on maximizing the expected return of the portfolio while minimizing its variance, assuming that risky asset returns are normally distributed. The normality assumption however has widely been criticized as actual stock price distributions exhibit much heavier tails as well as asymmetry. To this extent, in this paper we employ the genetic algorithm to find the optimal portfolio under the Value-at-Risk (VaR) constraint, where the tail of risky assets are modeled with the generalized Pareto distribution (GPD), the standard distribution for exceedances in extreme value theory. An empirical study using Korean stock prices shows that the performance of the proposed method is efficient and better than alternative methods.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.291-305
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• 2013

Collective traffic data (BPS, PPS etc.) for detection against the distributed denial of service attack on network is the time sequencing big data. The algorithm to detect the change point in the big data should be accurate and exceed in detection time and detection capability. In this work, the sliding window and discretization method is used to detect the change point in the big data, and propose five nonparametric test statistics using empirical distribution functions and ranks. With various distribution functions and their parameters, the detection time and capability including the detection delay time and the detection ratio for five test methods are explored and discussed via monte carlo simulation and illustrative examples.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.119-127
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• 2009

It has been known that bus route and company related characteristics have influences on punctuality, but fewer research have been conducted. Independent variables used in this study were selected using correlation analysis, and OLS(Ordinary Least Square) and HLM(Hierarchical Linear Model) were employed to identify factors affecting bus punctuality(headway deviation). The results showed that ICC(intraclass Correlation Coefficient) is 0.10, indicating that hierarchical linear models are more adequate for these data because there is effective variation in the subjects between companies. Punctuality was found to be negatively associated with the number of vehicles, the number of persons per vehicle, and total travel time. On the other hand, average headway and company size have a positive relationship with punctuality. Therefore, the number of vehicles per route, average headway, and the number of vehicles managed by a company should be considered for more accurately evaluating the management of piunctuality.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.183-194
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• 1997

In accelerated life tests, the failure time of an item is observed under a high stress level and based on the time, the failure rates of items we estimated at the normal stress level. In this paper, when the mean of the prior distribution of a parameter is known in Weibull lifetime model with censored failure time data, we study various estimating methods to obtain the empirical Bayes estimator of a parameter from the empirical Bayes approach under the normal stress level by considering the fact that the Bayes estimator is the function of prior parameters and of the acceleration parameter representing the effect of acceleration. And we compare the performance of several empirical Bayes estimators of a parameter in terms of the Bayes risk.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.247-264
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• 2008

This paper considers the integrated process control procedure for detecting special causes in an ARIMA(0,1,1) process that is being adjusted automatically after each observation using a minimum mean squared error adjustment policy. It is assumed that a special cause can change the process mean and the process variance. We derive expressions for the process deviation from target for a variety of different process parameter changes, and introduce a control chart, based on the generalized likelihood ratio, for detecting special causes. We also propose the integrated process control scheme bases on the future loss. The future loss denotes the cost that will be incurred in a process remaining interval from a true out-of-control signal.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.651-667
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• 2010

In this paper, using a meta analysis of anti-hyperlipidemia effect of soybeans were studied. Studied the effects of soybeans using Hedges' standardized mean difference looked at the effect. Applying the fixed-effects model analysis of fecal cholesterol and total cholesterol and triglycerides showed a statistically significant reduction in HDL cholesterol increase was statistically significant at. In addition, the homogeneity of all variables by running the test did not meet the homogeneity of the kidney weight, between weight, HDL cholesterol, LDL cholesterol, total cholesterol, and triglycerides in the random effects model against the results of the analysis conducted by a statistically significant variable that did not.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1439-1448
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• 2013

The analysis of variance for randomized block design or two way classification data is well known. In this paper, particularly, we considered two stage nested design in which the levels of one factor is not identical for different levels of another factor. We investigate the structural properties of two stage nested design and the properties of error sum of squares for random effect model. For the application of two way nested design, we consider two-period crossover design which is used commonly for the equivalence test to bio-similar product. The confidence interval estimation of the difference of two population means in the crossover design is discussed based on statistical package SPSS.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.456-456
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• 2012

한강유역에 위치한 247개의 강우계에서 관측된 강우 자료를 분석하여 Modified Bartlett-Lewis Retangular Pulse Model (MBLRPM)의 매개변수들을 산정하고, 이들의 지도를 작성한 후, 이들의 정확도 및 매개변수들의 시/공간적 변화 유형을 분석하였다. 이를 위한 첫번째 과정으로, 각 강우 게이지에 대해 MBLRPM의 매개변수에 사용되는 통계치 (각 달에 대한 1, 3, 12, 24시간 누적 수준에서의 평균, 분산, 자기 상관계수, 무강우 확률)들을 계산한다. 이 후, 격자화된 한강유역의 각 셀에 대하여 앞서 계산된 강우 통계치를 Ordinary Kriging 공간 보간법을 통하여 할당한다. 이 후, 각 셀에 할당된 강우 통계치를 사용하여 MBLRPM의 매개변수들을 산정하여 각 매개변수들의 지도를 각 달에 대하여 얻는다. 매개변수 지도를 사용하여 MBLRPM에 의해 생성된 강우 데이터들은 관측치의 통계치를 정확성있게 재현하였으며, 시/공간적 경향성을 분석한 결과, 강우세포의 지속기간과 관련된 매개 변수를 제외한 나머지 5개의 매개변수들은 확연한 공간적 경향성을 보인 한 편, 시간적 경향성은 잘 나타나지 않았다. 본 연구 결과는 매개변수 산정이 힘든 MBLRPM의 특성을 극복하게 해주어 가상 강우 생성을 용이하게 함으로써 강우에 영향을 받는 여러 종류의 연구 주제에 대해 불확실성 분석을 할 수 있게 한다는 점에서 의미를 가질 수 있다.