• 한국수자원학회논문집
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• 제47권9호
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• pp.743-752
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• 2014

함양 지체시간은 강우로부터 지표면을 지나 지하수면으로 도달하는 침투수의 통로 역할을 하는 비포화대를 통과할 때 발생하는 시간지연을 의미한다. 함양 지체시간을 직접적으로 측정하는 것은 불가능하기 때문에 본 연구는 고도와의 단순회귀분석을 이용하여 지체시간에 대한 경험식을 유도하였다. 이를 위하여 제주도 내에 4개의 유역(한천, 강정천, 외도천, 천미천)을 선정하여 총 18개의 관측지점에 대한 지체시간을 산정하였다. 또한 제안된 회귀식을 검증하기 위하여 선형 저수지 이론으로부터 유도된 방정식을 적용하여 구한 지체시간과 본 연구에서 유도된 경험식으로부터 산정된 지체시간을 이용하여 각각 산정한 지하수 함양량을 비교한 결과 상관성이 높은 것을 확인할 수 있었다. 따라서 본 연구에서 유도한 경험식을 이용하여 SWAT모형의 지체시간 매개변수에 적용할 경우 지하수 함양의 공간적 지연효과를 잘 반영할 것으로 판단된다.

• ETRI Journal
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• 제27권6호
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• pp.747-758
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• 2005

The problem of selection among competing models has been a fundamental issue in statistical data analysis. Good fits to data can be misleading since they can result from properties of the model that have nothing to do with it being a close approximation to the source distribution of interest (for example, overfitting). In this study we focus on the preference among models from a family of polynomial regressors. Three decades of research has spawned a number of plausible techniques for the selection of models, namely, Akaike's Finite Prediction Error (FPE) and Information Criterion (AIC), Schwartz's criterion (SCH), Generalized Cross Validation (GCV), Wallace's Minimum Message Length (MML), Minimum Description Length (MDL), and Vapnik's Structural Risk Minimization (SRM). The fundamental similarity between all these principles is their attempt to define an appropriate balance between the complexity of models and their ability to explain the data. This paper presents an empirical study of the above principles in the context of model selection, where the models under consideration are univariate polynomials. The paper includes a detailed empirical evaluation of the model selection methods on six target functions, with varying sample sizes and added Gaussian noise. The results from the study appear to provide strong evidence in support of the MML- and SRM- based methods over the other standard approaches (FPE, AIC, SCH and GCV).

• Journal of Biosystems Engineering
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• 제16권2호
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• pp.111-117
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• 1991

An empirical model for diesel engine control was obtained using a system identification method. A pseudo-random binary sequence was used as an input signal. Spectral anaylsis was used to find the frequency response of system. Model parameters of transfer functions were obtained using nonlinear regression.

• Journal of Preventive Medicine and Public Health
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• 제24권1호
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• pp.29-36
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• 1991

A multiple regression analysis using ordinary least square (OLS) is frequently used for the projection of health expenditure as well as for the identification of factors affecting health care costs. Data for the analysis often have mixed characteristics of time series and cross section. Parameters as a result of OLS estimation, in this case, are no longer the best linear unbiased estimators (BLUE) because the data do not satisfy basic assumptions of regression analysis. The study theoretically examined statistical problems induced when OLS estimation was applied with the time series cross section data. Then both the OLS regression and time series cross section regression (TSCS regression) were applied to the same empirical da. Finally, the difference in parameters between the two estimations were explained through residual analysis.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 춘계 학술발표회
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• pp.1043-1052
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• 2009

To derive easily the coefficient of permeability from several other soil properties, the estimation model of coefficient of permeability was proposed using linear regression analysis. The coefficient of permeability is one of the major factors to evaluate the soil characteristics. The study area is located in Kangwon-do Pyeongchang-gun Jinbu-Myeon. Soil samples of 45 spots were taken from the study area and various soil tests were carried out in laboratory. After selecting the soil factor influenced by the coefficient of permeability through the correlation analysis, the estimation model of coefficient of permeability was developed using the linear regression analysis between the selected soil factor and the coefficient of permeability from permeability test. Also, the estimation model of coefficient of permeability was compared with the results from permeability test and empirical equation, and the suitability of proposed model was proved. As the result of correlation analysis between various soil factors and the coefficient of permeability using SPSS(statistical package for the social sciences), the largest influence factor of coefficient of permeability were the effective grain size, porosity and dry unit weight. The coefficient of permeability calculated from the proposed model was similar to that resulted from permeability test. Therefore, the proposed model can be used in case of estimating the coefficient of permeability at the same soil condition like study area.

• 응용통계연구
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• 제23권4호
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• pp.669-681
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• 2010

Value-at-Risk(VaR) is an important part of risk management in the financial industry. This paper present a VaR forecasting for financial time series based on the quantile regression for GARCH models recently developed by Lee and Noh (2009). The proposed VaR forecasting features the direct conditional quantile estimation for GARCH models that is well connected with the model parameters. Empirical performance is measured by several backtesting procedures, and is reported in comparison with existing methods using sample quantiles.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.155-163
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• 2005

This paper applies Support Vector Regression (SVR) to estimate and forecast nonlinear autoregressive integrated (ARI) model of the daily exchange rates of four currencies (Swiss Francs, Indian Rupees, South Korean Won and Philippines Pesos) against U.S. dollar. The forecasting abilities of SVR are compared with linear ARI model which is estimated by OLS. Sensitivity of SVR results are also examined to kernel type and other free parameters. Empirical findings are in favor of SVR. SVR method forecasts exchange rate level better than linear ARI model and also has superior ability in forecasting the exchange rates direction in short test phase but has similar performance with OLS when forecasting the turning points in long test phase.

• 한국항만경제학회지
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• 제38권2호
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• pp.153-164
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• 2022

본 연구는 2000년 1월부터 2021년 12월까지의 대표적 원자재 운송 수단인 Capesize 중고선가를 대상으로 해운산업에 대한 분위수 모형을 추정한다. 본 연구는 두 가지 학술적 기여를 목표로 한다. 첫째, 혼재된 실증분석 결과가 제기되는 원자재 운송 시장의 대표적 선종인 Capesize 중고선과 운임시장의 연관성을 분석한다. 둘째, 분위수 회귀로 김현석·장명희(2020a) 연구에서 제기하는 구조변환을 고려하는 실증분석 모형을 제시한다. 분석 결과는 분위수 모형은 시계열 자료에서 구조변화를 분석에 반영함으로써 오차의 불안정성으로 제기되는 문제를 우회할 수 있음을 확인한다. 그리고 공적분 모형의 장기 균형관계를 장기와 단기 추정변수를 통해 외생변수의 장·단기 영향으로 구분하고, 이를 분위별로 세분화한 예측으로 확장한다. 이상의 추정결과는 해운 이론모형에 기반한 분석을 인공지능과 기계학습으로 확장할 수 있는 근거가 된다.

• Earthquakes and Structures
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• 제3권1호
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• pp.1-15
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• 2012

An empirical predictive relationship correlating bracketed duration to earthquake magnitude, site-to-source distance, and local site conditions (i.e. rock vs. stiff soil) for stable continental regions of North America is presented herein. The correlation was developed from data from 620 horizontal motions for central and eastern North America (CENA), consisting of 28 recorded motions and 592 scaled motions. The bracketed duration data was comprised of nonzero and zero durations. The non-linear mixed-effects regression technique was used to fit a predictive model to the nonzero duration data. To account for the zero duration data, logistic regression was conducted to model the probability of zero duration occurrences. Then, the probability models were applied as weighting functions to the NLME regression results. Comparing the bracketed durations for CENA motions with those from active shallow crustal regions (e.g. western North America: WNA), the motions in CENA have longer bracketed durations than those in the WNA. Especially for larger magnitudes at far distances, the bracketed durations in CENA tend to be significantly longer than those in WNA.

• The Journal of Asian Finance, Economics and Business
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• 제3권3호
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• pp.27-37
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• 2016

This study uses an endogenous economic growth model to determine the long run relationship between trade openness and economic growth in China by using the data 1975-2009.It contributes to the literature by developing trade openness index. An autoregressive distributed lag approach to cointegration and rolling regression method are employed. This study tests the link between trade openness and economic growth in the case of China by using the framework of endogenous economic growth model. This study also employs the rolling window regression method in order to examine the stability of coefficients throughout the sample span. The autoregressive distributed lag (ARDL) cointegration technique and rolling regression method are used. The empirical findings indicate that trade openness (i.e. Both individual trade indicator and composite trade openness index) are positively related to economic growth in the long run and short run. Our results indicate that trade openness as measured by individual trade indicator and composite trade openness index are positively related to economic growth in the long run and short run. However, results from the rolling window suggest that trade openness is negatively linked to economic growth only for a number of years.