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- 저자 : 김윤대(Kim, Yundae) (검색결과 5 건)
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김윤대(Kim, Yundae),전치혁(Jun, Chi-Hyuck) 한국경영과학회 2010 한국경영과학회 학술대회논문집 Vol.2010 No.6
Panel data is a type of data that includes time-series and cross-sectional dimension. To analyze panel data, it should be known that it is stationary or non-stationary data. If the data is non-stationary and analyzed directly, it may lead to error. The panel unit root test determines if panel data is stationary or not. Many types of unit root test of panel data have been developed which included IPS unit root test and Fisher’s test. This paper presents a new panel unit root test using false discovery rate (FDR). After proposing the new model, this paper compares it with IPS and other models by some artificial data. It is concluded that the new model has similar power of test as compared with other tests.
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김윤대(Kim, Yundae),이혜선(Lee, Hyeseon),전치혁(Jun, Chi-Hyuck) 한국경영과학회 2011 한국경영과학회 학술대회논문집 Vol.2011 No.5
Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and partial least squares classification methods by some real data. It is concluded that the new method has better power as compared with other methods.
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김윤대(Kim, Yundae),이혜선(Lee, Hyeseon),전치혁(Jun, Chi-Hyuck) 대한산업공학회 2011 대한산업공학회 춘계학술대회논문집 Vol.2011 No.5
Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and partial least squares classification methods by some real data. It is concluded that the new method has better power as compared with other methods.
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김윤대(Kim, Yundae),전치혁(Jun, Chi-Hyuck) 대한산업공학회 2010 대한산업공학회 춘계학술대회논문집 Vol.2010 No.6
Panel data is a type of data that includes time-series and cross-sectional dimension. To analyze panel data, it should be known that it is stationary or non-stationary data. If the data is non-stationary and analyzed directly, it may lead to error. The panel unit root test determines if panel data is stationary or not. Many types of unit root test of panel data have been developed which included IPS unit root test and Fisher’s test. This paper presents a new panel unit root test using false discovery rate (FDR). After proposing the new model, this paper compares it with IPS and other models by some artificial data. It is concluded that the new model has similar power of test as compared with other tests.
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이상호(Sang-Ho Lee),이혜선(Hyeseon Lee),김윤대(Yun-Dae Kim),전치혁(Chi-Hyuck Jun) 대한산업공학회 2010 대한산업공학회지 Vol.36 No.2
The partial least squares (PLS) method is popularly used for estimating the structural equation model, but the existing algorithm may not be directly implemented when probabilities are involved in some constructs or manifest variables. We propose a structural equation model including the brand choice as one construct having brand choice probabilities as its manifest variables. Then, we develop a PLS-based algorithm for the structural equation model by utilizing the multinomial logit model. A case is introduced as an application and simulation studies are performed to validate the proposed algorithm.
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