• Journal of the Korean Statistical Society
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• 제27권1호
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• pp.1-10
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• 1998

In the problem of estimating a vector of unknown regression coefficients under the sum of squared error losses in a hierarchical linear model, we propose the hierarchical Bayes estimator of a vector of unknown regression coefficients in a hierarchical linear model, and then prove the admissibility of this estimator using Blyth's (196\51) method.

• 환경영향평가
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• 제8권2호
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• pp.73-82
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• 1999

This study analyzed the relationship between NDVI(Normalized Difference Vegetation Index) and urban heat island in three cities: Daegu, Kyungju, and Pohang for understanding the degree of nature conservation concentrating in the transition zone of them. Daegu city is the third city in Korea which has a dense population. Kyungju is a traditional city which has good nature. Pohang is an industrial city which has those of characters of Daegu and Kyungju. Landsat 1M data in May 17, 1997 were used for the analysis of heat island. There were about four theoretical models to estimate the surface temperature from TM data: Two-point linear model, Linear regression model, Quadratic regression model, and Cubic regression model. In this study, Linear regression model had been utilized to analyze the urban heat island. On the resultant images, the transition zone of Daegu was urbanized more extremely than those of other two cities. It is thought that the analysis of relationship between NDVI and surface temperature, used in this study, is regarded as one of effective methodologies for urban-environmental detection from satellite imageries.

• Communications for Statistical Applications and Methods
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• 제25권2호
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• pp.199-215
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• 2018

Interest in $PM_{10}$ concentrations have increased greatly in Korea due to recent increases in air pollution levels. Therefore, we consider a forecasting model for next day $PM_{10}$ concentration based on the principal elements of air pollution, weather information and Beijing $PM_{2.5}$. If we can forecast the next day $PM_{10}$ concentration level accurately, we believe that this forecasting can be useful for policy makers and public. This paper is intended to help forecast a daily mean $PM_{10}$, a daily max $PM_{10}$ and four stages of $PM_{10}$ provided by the Ministry of Environment using various data mining techniques. We use seven models to forecast the daily $PM_{10}$, which include five regression models (linear regression, Randomforest, gradient boosting, support vector machine, neural network), and two time series models (ARIMA, ARFIMA). As a result, the linear regression model performs the best in the $PM_{10}$ concentration forecast and the linear regression and Randomforest model performs the best in the $PM_{10}$ class forecast. The results also indicate that the $PM_{10}$ in Seoul is influenced by Beijing $PM_{2.5}$ and air pollution from power stations in the west coast.

• 대한기계학회논문집A
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• 제24권10호
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• pp.2589-2596
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• 2000

As current manufacturing processes require high spindle speed and precise machining, increasing accuracy by reducing volumetric errors of the machine itself, particularly thermal errors, is very important. Thermal errors can be estimated by many empirical models, for example, an FEM model, a neural network model, a linear regression model, an engineering judgment model, etc. This paper discusses to make a modeling of thermal errors efficiently through backward elimination and fuzzy logic strategy. The model of a thermal error using fuzzy logic strategy overcomes limitation of accuracy in the linear regression model or the engineering judgment model. It shows that the fuzzy model has more better performance than linear regression model, though it has less number of thermal variables than the other. The fuzzy model does not need to have complex procedure such like multi-regression and to know the characteristics of the plant, and the parameters of the model can be mathematically calculated. Also, the fuzzy model can be applied to any machine, but it delivers greater accuracy and robustness.

• 응용통계연구
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• 제29권5호
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• pp.773-786
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• 2016

분위수 회귀는 반응변수의 조건부 분위수 함수를 추정함으로써 반응변수와 예측변수의 관계에 대한 포괄적인 정보를 제공한다. 그러나 여러 개의 분위수 함수를 개별적으로 추정하게 되면 이들이 서로 교차할 가능성이 있으며, 이러한 분위수 함수의 교차(quantile crossing) 현상 분위수의 이론적 기본 특성에 위배된다. 본 논문에서는 다중 비교차 분위수 함수의 추정의 대표적인 방법들의 특성을 적합식과 계산 알고리즘의 측면에서 살펴보고, 모의실험과 실제 자료 분석을 통해 그 성능을 비교하였다.

• 전기학회논문지
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• 제59권6호
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• pp.1041-1045
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• 2010

Short-term electric load forecasting of power systems is essential for the power system stability and the efficient power system operation. An accurate load forecasting scheme improves the power system security and saves some economic losses in power system operations. Due to scarcity of the historical same type of holiday load data, most big electric load forecasting errors occur on load forecasting for the holidays. The fuzzy linear regression model has showed good accuracy for the load forecasting of the holidays. However, it is not good enough to forecast the load of the election day. The concept of the load variation rate for the load forecasting of the election day is introduced. The proposed algorithm shows its good accuracy in that the average percentage error for the short-term 24 hourly loads forecasting of the election days is 2.27%. The accuracy of the proposed 24 hourly loads forecasting of the election days is compared with the fuzzy linear regression method. The proposed method gives much better forecasting accuracy with overall average error of 2.27%, which improved about average error of 2% as compared to the fuzzy linear regression method.

• 드라이브 ㆍ 컨트롤
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• 제20권4호
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• pp.133-139
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• 2023

A weighing system calculates the bucket's excavation amount of an excavator. Usually, the excavation amount is computed by the excavator's motion equations with sensing data. But these motion equations have computing errors that are induced by assumptions to the linear systems and identification of the equation's parameters. To reduce computing errors, some commercial weighing system incorporates particular motion into the excavation process. This study introduces a linear regression model on an artificial neural network that has fewer predicted errors and doesn't need a particular pose during an excavation. Time serial data were gathered from a 30tons excavator's loading test. Then these data were preprocessed to be adjusted by MPL (Multi Layer Perceptron) or CNN (Convolutional Neural Network) based linear regression models. Each model was trained by changing hyperparameter such as layer or node numbers, drop-out rate, and kernel size. Finally ID-CNN-based linear regression model was selected.

• 품질경영학회지
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• 제47권4호
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• pp.687-700
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• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• 한국수자원학회논문집
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• 제42권6호
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• pp.465-480
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• 2009

본 연구에서는 다중회귀분석을 이용하여 산악효과를 야기하는 지형인자와 강수와의 관계를 파악하였다. 섬 전체가 산악지형인 제주도의 연평균강수량과 지수홍수법으로 산출한 확률강우량을 강수자료로 사용하여 산악효과를 야기하는 지형인자로 선정한 고도, 위 경도와 회귀모형을 구성하였다. 회귀분석 결과 연평균강수량과 고도와의 선형관계가 확률강우량에서도 동일하게 나타났으며, 고도이외에 위도, 경도를 각각 추가인자로 고려할 경우 강우량과 더욱 강한 상관성을 보였다. 또한, 고도와 위도, 경도를 모두 고려한 회귀모형을 이용한 지형공간분석 결과 제주도의 실제 강수특성과 마찬가지로 남동부로 편중된 강수형태를 보여 모형의 적합성을 증명하였다. 그러나 지속시간 및 재현기간과 무관하게 높은 고도에서 회귀식의 유효성이 감소하므로, 높은 고도에서의 추가적인 산악효과인자의 강수량에 대한 영향이 존재될 것으로 판단되므로 추후 연구가 필요하다.

• Journal of the Korean Statistical Society
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• 제26권3호
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• pp.299-308
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• 1997

In the simple linear regression model Y = .alpha.$_{0}$ + .beta.$_{0}$Z + .epsilon. under the right censorship of the response variables, the estimation of the mean lifetime E(Y) is an interesting problem. In this paper we propose a method of estimating E(Y) based on the observations modified by the arguments of Buckley and James (1979). It is shown that the proposed estimator is consistent and our proposed procedure in the simple linear regression case can be naturally extended to the multiple linear regression. Finally, we perform simulation studies to compare the proposed estimator with the estimator introduced by Gill (1983).83).