• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.217-233
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• 1996

This paper gives a rigorous proof of an asymptotic result about bias and variance for a transformation-based nonparametric regression estimator proposed by Park et al (1995).

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.971-980
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• 2017

The varying coefficient regression model has gained lots of attention since it is capable to model dynamic changes of regression coefficients in many regression problems of science. In this paper we propose a varying coefficient regression model that effectively considers the errors on both input and response variables, which utilizes the kernel method in estimating the varying coefficient which is the unknown nonlinear function of smoothing variables. We provide a generalized cross validation method for choosing the hyper-parameters which affect the performance of the proposed model. The proposed method is evaluated through numerical studies.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1045-1054
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• 2012

Support vector machine is known to be the very useful statistical method in classification and nonlinear function estimation. In this paper we propose a monotone support vector regression (SVR) for the estimation of monotonically decreasing function. The proposed monotone SVR is applied to smooth the Kaplan-Meier estimate of survival function. Experimental results are then presented which indicate the performance of the proposed monotone SVR using survival functions obtained by exponential distribution.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.95-103
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• 1998

Azzalini and Bowman proposed the pseudo-likelihood ratio test for checking the linear relationship using kernel regression estimator when the error of the regression model follows the normal distribution. We modify their method with the bootstrap technique to construct a new test, and examine the power of our test through simulation. Our method can be applied to the case where the distribution of the error is not normal.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.439-441
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• 2005

A kernel regression filter with adaptive bandwidth is developed and successfully applied to digital reactivity meter for neutron signal measurement in nuclear reactors. The purpose of this work is not only reduction of the measurement noise but also the edge preservation of the reactivity signal. The performance of the filtering algorithm is demonstrated comparing with well known smoothing methods of conventional low-pass and bilateral filters. The developed method gives satisfactory filtering performance and edge preservation capability.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.149-158
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• 1994

Nonparametric regression technique using kernel estimator is an attractive alternative that has received some attention, recently. The kernel estimate depends on two quantities which have to be provided by the user : the kernel function and the bandwidth. However, the more difficult problem is how to find an appropriate bandwidth which controls the amount of smoothing (see Silverman, 1986). Thus, in practical situation, it is certainly desirable to determine an appropriate bandwidth in some automatic fashion. Thus, the problem is to find a data-driven or adaptive (i.e., depending only on the data and then directly computable in practice) bandwidth that performs reasonably well relative to the best theoretical bandwidth. In this paper, we introduce a relation between bias and variance of mean square error. Thus, we present a simple and effective algorithm for selecting local bandwidths in kernel regression.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.231-246
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• 2001

Local linear smoothing enjoys several excellent theoretical and numerical properties, an in a range of applications is the method most frequently chosen for fitting curves to noisy data. Nevertheless, it suffers numerical problems in places where the distribution of design points(often called predictors, or explanatory variables) is spares. In the case of univariate design, several remedies have been proposed for overcoming this problem, of which one involves adding additional ″pseudo″ design points in places where the orignal design points were too widely separated. This approach is particularly well suited to treating sparse bivariate design problem, and in fact attractive, elegant geometric analogues of unvariate imputation and interpolation rules are appropriate for that case. In the present paper we introduce and develop pseudo dta rules for bivariate design, and apply them to real data.

• Journal of Mechanical Science and Technology
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• v.16 no.3
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• pp.395-402
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• 2002

This paper shows how to formulate the transient analysis of 2-dimensional Hagen-Poiseuille flow using smoothed particle hydrodynamics (SPH). Treatments of viscosity, particle approximation and boundary conditions are explained. Numerical tests are calculated to examine effects caused by the number of particles, the number of particles per smoothing length, artificial viscosity and time increments for 2-dimensional Hagen-Poiseuille flow. Artificial viscosity for reducing the numerical instability directly affects the velocity of the flow, though effects of the other parameters do not produce as much effect as artificial viscosity. Numerical solutions using SPH show close agreement with the exact ones for the model flow, but SPH parameter must be chosen carefully Numerical solutions indicate that SPH is also an effective method for the analysis of 2-dimensional Hagen-Poiseuille flow.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.993-1000
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• 2016

Semi-supervised learning makes it easy to use an unlabeled data in the supervised learning such as classification. Applying the semi-supervised learning on the regression analysis, we propose two methods for a better regression function estimation. The proposed methods have been assumed different marginal densities of independent variables and different smoothing parameters in unlabeled and labeled data. We shows that the overfitted pilot estimator should be used to achieve the fastest convergence rate and unlabeled data may help to improve the convergence rate with well estimated smoothing parameters. We also find the conditions of smoothing parameters to achieve optimal convergence rate.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.14-14
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• 2003

Given a sequence ｛ $X_{n}$｝ of independent and identically distributed random variables with F, a sequential procedure for the p-th quantile ξ$_{P}$= $F^{-1}$ (P), 0$\beta$-protection. Some asymptotic properties for the proposed procedure and of an involved stopping time are proved: asymptotic consistency, asymptotic efficiency and asymptotic normality. From one of the results an effect of smoothing based on kernel functions is discussed. The results are also extended to the contaminated case.e.e.