• Bulletin of the Korean Mathematical Society
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• v.52 no.2
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• pp.679-684
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• 2015

In this paper we discuss bounds for the total curvature of nonparametric minimal surfaces by using the properties of planar harmonic mappings.

• ETRI Journal
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• v.15 no.2
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• KDI Journal of Economic Policy
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• v.34 no.2
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• pp.139-171
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• 2012

This paper examines the effect of private tutoring expenditures on academic performance of middle school students in South Korea, using data from "Analysis of the Level of School Education and Its Actual condition: Middle School". In the face of endogeneity of private tutoring expenditures, the paper employs an instrumental variable (IV) method and a nonparametric bounding method. Using both methods we show that the true effect of private tutoring on middle school students remains at most modest in Korea. The IV results suggest that a 10 percent increase in tutoring expenditure for Korean, English and math raises a student's test score of the subject at the largest by 1.24, 1.28, and 0.75 percent, respectively. The bounding results also fail to show evidence that an increase in tutoring expenditure leads to economically and statistically significant improvements in test score.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.633-641
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• 2005

In this paper, we address the derivation of joint distributions and correlation coefficients for three pairs of statistics used commonly in a number of signal detection schemes. The upper and lower bounds of the correlation coefficients for the three pairs are obtained, and interesting relationships between the correlation coefficients are derived. Explicit values of the correlation coefficients evaluated for some meaningful distributions are given in the form of tables and figures for easy reference. The results in this paper should be useful in comparing various detection statistics.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.684-701
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• 2016

The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.541-550
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• 2005

In this paper, we address the derivation of joint distributions and correlation coefficients for four pairs of statistics used commonly in a number of signal detection schemes. The upper and lower bounds of the correlation coefficients are obtained, and interesting relationships between the correlation coefficients are derived. Explicit values of the correlation coefficients are given in the form of tables and figures for easy reference. The results in this paper should be useful in comparing various detection statistics.