• Annual Conference of KIPS
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• 2001.10a
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• pp.515-518
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• 2001

본 논문에서는 전처리단계로 영평균 정규화 기법과 주요성분분석 기법을 도입하여 다층신경망을 이용한 고신뢰성의 회귀분석 모델을 제안한다. 영평균 정규화 기법은 데이터의 1차적 통계성을 고려하여 알고리즘을 간략화시키며, 주요성분분석 기법은 입력 데이터의 2차적 통계성을 고려하여 독립인 특징들의 집합으로 변환시켜 학습데이터의 차원을 감소시킬 수 있어 고차원의 학습데이터에 따른 회귀분석 모델의 제약을 해결할 수 있었다. 제안된 기법의 신경망을 3개의 독립변수를 가진 암모니아 제조공정문제와 10개의 독립변수를 가진 자동차 연비문제에 각각 적용하여 시뮬레이션한 결과, 단순정규화나 PCA를 적용하지 않는 경우보다 제안된 기법의 학습속도와 회귀성능이 더욱 더 우수함을 확인할 수 있었다.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.527-535
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• 1999

양적 확률응답을 이용한 민감사안에 대한 평균이나 분석의 추정시 보조정보를 활용한 회귀추정법에 대해서 언급하고, 유도된 회귀추정량과 Greenberg et al.의 추정량 그리고 비추정량과의 비교의 통하여 회귀추정량이 효율적일 수 있는 조건을 찾았다. 또한 각 질문에 대한 응답의 분포가 포아송 분포인 경우 회귀추정량의 효율이 증대될 수 있는 조건에 대해서도 논하였다.

• Environmental and Resource Economics Review
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• v.26 no.4
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• pp.499-517
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• 2017

The purpose of this article is to show profit margin hedging can be an optimal strategy in crude oil purchasing. This study theoretically analyzes profit margin hedging strategy is optimal in crude oil purchasing using expected target utility function and conducts simulations to show if the profit margin hedging is profitable. In addition, this study tests existence of mean reversion of crude oil futures prices to confirm the theory that profit margin hedging is more profitable than other strategies, such as always hedging or buying at expiration with spot price, if futures prices are mean reverting. The simulation results show that the expected utility of profit margin hedging higher than other strategies. Although we cannot find any evidence that crude oil futures prices follow mean reverting process, we can conclude that profit margin hedging can be optimal strategy in crude oil purchasing based on theoretical proof and simulation results.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.969-977
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• 2016

Climate change affects the growth of crops which were planted especially in fields, and it becomes more important to use climate data to predict the yields of the major vagetables. The variation of the crop products caused by climate change is one of the significant factors for the discrepancy of the demand and supply, and leads to the price instability. In this paper, using a panel regression model, we predicted the garlic yields with the weather conditions of different regions. More specifically we used the panel data of the several climate variables for 15 main garlic production areas from 2006 to 2015. Seven variables (average temperature, average maximum temperature, average minimum temperature, average surface temperature, cumulative precipitation, average relative humidity, cumulative duration time of sunshine) for each month were considered, and most significant 7 variables were selected from the total 84 variables by the stepwise regression. The random effects model was chosen by the Hausman test. The average maximum temperature (January), the cumulative precipitation (March, October), the cumulative duration time of sunshine (April, October) were chosen among the variables as the significant climate variables of the model

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.267-277
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• 2016

This paper considers we consider the estimation of copula parameters based on residuals in stochastic regression models. We prove that a semiparametric estimator using residual empirical distributions is consistent under some conditions and apply the results to the copula-ARMA model. We provide simulation results for illustration.

• The KIPS Transactions:PartB
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• v.8B no.4
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• pp.327-334
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• 2001

본 논문에서는 기울기하강과 동적터널링이 조합된 학습알고리즘의 다층신경망을 이용한 고신회성의 회귀분석 모델을 제안하였다. 기울기하강은 빠른 수렴속도의 최적화가 가능하도록 하기 위함이고, 동적터널링은 국소최적해를 만났을 때 이를 벗어난 새로운 연결가중치를 설정하여 전역최적해로 수렴되도록 하기 위함이다. 또한 대용량의 입력 데이터를 통계적으로 독립인 특징들의 집합으로 변환시키는 주요성분분석 기법의 속성을 살려 학습데이터의 차원을 감소시킴으로서 고차원의 학습데이터에 따른 회귀분석 모델의 제약도 동시에 해결하였다. 제안된 기법의 신경망을 3개의 독립변수 패턴을 가진 암모니아 제조공정문제와 10개의 독립변수 패턴을 가진 자동차 연비문제에 각각 적용하여 시뮬레이션한 결과, 기존의 역전과 알고리즘의 신경망이나 주요성분분석에 의한 차원을 감소시키지 않은 학습패턴을 이용한 신경망보다 각각 더욱 우수한 학습성능과 회귀성능이 있음을 확인할 수 있었다. 또한 학습패턴의 영평균 정규화로 회귀용 신경망의 성능을 더욱 더 개선하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.169-173
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• 2008

본 연구는 저수량 지역 빈도분석(regional low flow frequency analysis)을 수행하기 위하여 일반최소자승법(ordinary least squares method)을 이용한 Bayesian 다중회귀분석을 적용하였으며, 불확실성측면에서의 효과를 탐색하기 위하여 Bayesian 다중회귀분석에 의한 추정치와 t 분포를 이용하여 산정한 일반 다중회귀분석의 추정치의 신뢰구간을 비교분석하였다. 각 재현기간별 비교결과를 보면 t 분포를 이용하여 산정된 평균 추정치와 Bayesian 다중회귀분석에 의한 평균 추정치는 크게 다르지 않았다. 그러나 불확실성 측면에서 평가해볼 때 신뢰구간의 상한추정치와 하한추정치의 차이는 Bayesian 다중회귀분석을 사용한 경우가 기존 방법을 사용한 경우보다 훨씬 작은 것으로 나타났으며, 이로부터 저수량(low flow) 지역 빈도분석을 수행하는 경우 Bayesian 다중회귀분석이 일반 회귀분석보다 불확실성을 표현하는데 있어서 우수하다는 결과를 얻을 수 있었다. 또한 낙동강 유역에 2개의 미계측 유역을 선정하고 구축된 Bayesian 다중회귀모형을 적용하여 불확실성을 포함한 미계측 유역에서의 저수량(low flow)을 추정하였으며 이와 같은 방법이 미계측 유역에서의 저수(low flow) 특성을 나타내는 데 있어서 효과적일 수 있음을 입증하였다.

• Journal of Bio-Environment Control
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• v.17 no.2
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• pp.124-131
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• 2008

To investigate main air temperature factors correlated to difference of fruit characteristics according to cultivating areas, fruit and air temperature characteristics of eight cultivating areas of 'Fuyu' persimmon were analyzed by principle components and multiple regression analysis. The first principal components extracted from 16 air temperature factors was annual mean temperature, mean temperature during October, annual mean minimum extreme temperature, mean temperature during growing period, and so forth. The second principal components was mean temperature during May and June and so forth. And cumulative contribution was 91.4%. The five of eight cultivating area had clearly the difference of main factors or the correlated direction among cultivating areas. In multiple regression analysis between the extracted main factors and fruit characteristics, fruit hight were highly correlated with mean temperature during growing period ($X_8$) and cumulative temperature ($X_6$), and the regression equation was $Y=150.55-5.375X_8+ 0.014X_6(r^2=0.843)$. Also this regression equation was affected by mean minimum temperature during growing period, cumulative temperature, and mean temperature during August. Fruit diameter was negatively correlated with mean temperature during growing period, flesh browning rate and Hunter a value of peel color were positively correlated with mean minimum temperature during growing period and annual minimum air temperature, respectively.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.733-745
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• 2017

Local composite quantile regression is a useful non-parametric regression method widely used for its high efficiency. Data smoothing methods using kernel are typically used in the estimation process with performances that rely largely on the smoothing parameter rather than the kernel. However, $L_2$-norm is generally used as criterion to estimate the performance of the regression function. In addition, many studies have been conducted on the selection of smoothing parameters that minimize mean square error (MSE) or mean integrated square error (MISE). In this paper, we explored the optimality of selecting smoothing parameters that determine the performance of non-parametric regression models using local linear composite quantile regression. As evaluation criteria for the choice of smoothing parameter, we used mean absolute error (MAE) and mean integrated absolute error (MIAE), which have not been researched extensively due to mathematical difficulties. We proved the uniqueness of the optimal smoothing parameter based on MAE and MIAE. Furthermore, we compared the optimal smoothing parameter based on the proposed criteria (MAE and MIAE) with existing criteria (MSE and MISE). In this process, the properties of the proposed method were investigated through simulation studies in various situations.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1193-1201
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• 2020

This study aims to develop a regression model that estimates a low-flow index that can be applied to ungauged basins. A total of 30 midsized basins in South Korea use long-term runoff data provided by the National Integrated Water Management System (NIWMS) to calculate average low-flow, average minimum streamflow, and low-flow index duration and frequency. This information is used in the correlation analysis with 18 basin factors and 3 climate change factors to identify the basin area, average basin altitude, average basin slope, water system density, runoff curve number, annual evapotranspiration, and annual precipitation in the low-flow index regression model. This study evaluates the model's accuracy by using the root-mean-square error (RMSE) and the mean absolute error (MAE) for 10 ungauged, verified basins and compares them with the previous model's low-flow calculations to determine the effectiveness of the newly developed model. Comparative analysis indicates that the new regression model produces average low-flow, attributed to the consideration of varied basin and hydrologic factors during the new model's development.