• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.495-506
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• 1995

We present formula for detecting influential observations on the smoothing parameter in smoothing spline. Further, we express them as functions of basic building blocks such as residuals and leverage, and compare it with the local influence approach by Thomas (1991). An example based on a real data set is given.

• Proceedings of the Korea Concrete Institute Conference
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• 1996.10a
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• pp.31-36
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• 1996

Various strength characteristics of recycled concretes containing different contents of recycled aggregates from waste concretes were compared with one another. Five different contents. 0%, 50%, 60%, 70% and 80%, of recycled concrete were used for this study. Study results showed that the compressive strength, flexural strength, tensile strength, elastic modulus and fracture toughness varied with contents of recycled aggregates. Target strength of the recycled concrete could be difined by nonparametric regression model as a funcion of content of recycled aggregate in the mix.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.201-210
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• 2004

The peakedness ordering is closely related to dispersive ordering. In this paper we consider the statistical inference concerning peakedness ordering between two arbitrary symmetric distributions. Nonparametric maximum likelihood estimates of two distribution functions under symmetry and peakedness ordering are given. The likelihood ratio test for equality of two symmetric discrete distributions in the sense of peakedness ordering is studied.

• Proceedings of the Korea Database Society Conference
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• 1998.09a
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• pp.584-594
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• 1998

선형회귀분석은 가장 널리 사용되는 데이터 분석기법이지만 독립변수와 종속변수간의 관계가 선형이라고 가정하기 때문에 문제점을 가지고 있다. 비모수 회귀분석(Nonparametric Regression)은 선형회귀분석의 문제점을 극복할 수 있는 방법으로 변수간의 관계의 형태를 미리 가정하지 않고 데이터에 의해 결정하는 방법이다. 본 연구에서는 유전자 알고리즘을 비모수 회귀분석법 중의 하나인 Regressoin Splines에 적용하였다. 인위적 데이터를 이용한 평가 결과 유전자 알고리즘은 다양한 상황에서 매우 우수한 것으로 나타났다.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.669-678
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• 2007

An algorithm to detect the number of discontinuity points of the variance function in regression model is proposed. The proposed algorithm is based on the left and right one-sided kernel estimators of the second moment function and test statistics of the existence of a discontinuity point coming from the asymptotic distribution of the estimated jump size. The finite sample performance is illustrated by simulated example.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.451-457
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• 2010

This paper considers the problem of nonparametric deconvolution density estimation when sample observa-tions are contaminated by double exponentially distributed errors. Three different deconvolution density estima-tors are introduced: a weighted kernel density estimator, a kernel density estimator based on the support vector regression method in a RKHS, and a classical kernel density estimator. The performance of these deconvolution density estimators is compared by means of a simulation study.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1423-1430
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• 2017

Detonation cell width is an important parameter in hydrogen explosion assessments. The experimental data on gas detonation are statistically analyzed to establish a universal method to numerically predict detonation cell widths. It is commonly understood that detonation cell width, ${\lambda}$, is highly correlated with the characteristic reaction zone width, ${\delta}$. Classical parametric regression methods were widely applied in earlier research to build an explicit semiempirical correlation for the ratio of ${\lambda}/{\delta}$. The obtained correlations formulate the dependency of the ratio ${\lambda}/{\delta}$ on a dimensionless effective chemical activation energy and a dimensionless temperature of the gas mixture. In this paper, support vector regression (SVR), which is based on nonparametric machine learning, is applied to achieve functions with better fitness to experimental data and more accurate predictions. Furthermore, a third parameter, dimensionless pressure, is considered as an additional independent variable. It is found that three-parameter SVR can significantly improve the performance of the fitting function. Meanwhile, SVR also provides better adaptability and the model functions can be easily renewed when experimental database is updated or new regression parameters are considered.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.937-944
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• 2021

The problem of selecting variables is addressed in partial linear regression. Model selection for partial linear models is not easy since it involves nonparametric estimation such as smoothing parameter selection and estimation for linear explanatory variables. In this work, several approaches for variable selection are proposed using a fast forward selection algorithm, least angle regression (LARS). The proposed procedures use t-test, all possible regressions comparisons or stepwise selection process with variables selected by LARS. An example based on real data and a simulation study on the performance of the suggested procedures are presented.

• Journal of Acupuncture Research
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• v.20 no.1
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• pp.144-158
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• 2003

This study was carried out to investigate what kinds of statistical techniques have been used to analyze data from oriental medicine research, For study, 551 original articles which used statistical techniques in their data analysis were selected form the articles published in The journal of Korean Acupuncture & Moxibustion Society(JKAMS) between 1984 to 2002. among them, 122 articles used descriptive statistics while 429 articles used inferential statistics for data analysis. For that 429 articles, t-test (189 articles), analysis fo variance (111 articles), chi-square test (14 articles), correlation (10 articles), regression analysis (4 articles), factor analysis(5 articles), or nonparametric test (23 articles) were chose to analyze the data. Nonparametric approach has substantial power in case data do not meet the assumption of normality. This method is not only easy to use ut also provides measures of the statistical variation of nominal and ordinal scale. This study shows that more and more recent papers use nonparametric test compared to the old articles. nine different statistical software or packages (SAS, SPSS, Statview, Minitab, Sigma plot, ISP, Graphpad prism, Excel, Access) have been used in the articles published JKMAS. High level statistical techniques such as SAS, SPSS, and Statview are user friendly and used most for acupuncture and Moxibustion research. Including tables and plots in an article facilitates understanding family process data from a descriptive standpoint, minimized erroneous statistical conclusions, and clarifies theoretically important relationships among variables. Table and plots have been used 500 and 233 articles, respectively. A computer procedure is proposed and illustrated with statistical packages using SAS, SPSS, Statview and ISP.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.33-39
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• 2013

In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.