• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.765-774
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• 1997

본 논문에서는 반복이 없는 이원배치에서 교호작용의 존재를 가정하고 처리수준간의 모집단 분포의 동일성을 검정하는 비모수적 검정법을 제안하였다. 검정통계량의 구성을 위하여 순위벡터를 그 구조의 형태별로 정리한 순위위치벡터를 제안하고, 이의 특성과 응용가능성을 연구하였다. 또한 모의 검정력 연구를 통하여 기존의 비모수적 방법이 갖는 약점과 제안한 통계량의 우수함을 실증하였다.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.373-380
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• 2016

Even though Likert type data is ordinal scale, many researchers who regard Likert type data as interval scale adapt as parametric methods. In this research, simulations have been used to find out a proper analysis of Likert type data. The locations and response distributions of five point Likert type data samples having diverse distribution have been evaluated. In estimating samples' locations, we considered parametric method and non-parametric method, which are t-test and Mann-Whitney test respectively. In addition, to test response distribution, we employed Chi-squared test and Kolmogorov-Smirnov test. In this study, we assessed the performance of the four aforementioned methods by comparing Type I error ratio and statistical power.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.15-21
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• 2015

In support vector machine, the values of parameters included in kernels affect strongly generalization ability. It is often difficult to determine appropriate values of those parameters in advance. It has been observed through our studies that the burden for deciding the values of those parameters in support vector regression can be reduced by utilizing ensemble learning. However, the straightforward application of the method to large scale problems is too time consuming. In this paper, we propose a method in which the original data set is decomposed into a certain number of sub data set in order to reduce the burden for parameter tuning in support vector regression with large scale data sets and imbalanced data set, particularly.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.184-188
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• 2008

추계학적 시계열 분석은 크게 수문자료의 장기간 합성과 실시간 예측으로 구분해 볼 수 있다. 장기간 합성은 주로 수문자료의 추계적 특성을 반영한 수자원 시스템의 운영율 개발에 이용되어 왔다. 반면에 실시간 예측은 수자원 시스템의 순응적(adaptive) 관리에 적용되고 있다. 두 개념의 차이로 전자는 시계열 자료를 합성하여 발생 가능한 모든 수문조합을 얻고자 하는 것이라면 후자는 전 시간의 수문량을 조건으로 하는 다음 시간의 값을 순응적으로 예측하는 것이라 할 수 있다. 수문자료의 합성과 예측에는 크게 결정론적, 확률론적 방법의 두 가지 대별될 수 있다. 결정론적 모델링 방법에는 인공신경망이나 Fuzzy 기법 등을 이용할 수 있으며, 확률론적 방법에는 ARMAX 등의 모수적 기법과 k-NN(k-nearest neighbor bootstrap resampling), KDE(kernel density estimates), 추계학적 인공신경망 등의 비모수적 기법으로 분류할 수 있다. 본 연구에서는 대표적 비모수적 기법인 k-NN를 이용하여 충주댐을 대상으로 월 및 일 유입량 자료의 예측 정도를 살펴보았다. 전 시간 관측치를 조건으로 하는 다음 시간의 조건부 확률분포를 구하여 평균값을 계산한 후 관측치와 비교함으로써 모형의 정도를 살펴보았다. 그리고 실시간 저수지 운영에 이 기법의 활용성과 장단점도 살펴보았다. 모형개발 절차로 모형의 보정을 거쳐 검증을 실시하였다. 결론적으로 월 및 일 유입량 예측에 k-NN 기법이 실무적으로 적용될 수 있었으며, 장점으로는 k-NN 기법이 다른 기법보다 모델링 절차가 비교적 쉬워 저수지 운영 최적화 등 타 시스템과의 연계에 수월함이 인식되었다.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1129-1134
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• 2016

This paper suggests a new method of finding regularization parameter for image restoration problems. If the prior information is not available, separate optimization functions for Tikhonov regularization parameter are suggested in the literature such as generalized cross validation and L-curve criterion. In this paper, unified Bayesian interpretation of Tikhonov regularization is introduced and applied to the image restoration problems. The relationship between Tikhonov regularization parameter and Bayesian hyper-parameters is established. Update formular for the regularization parameter using both maximum a posteriori(: MAP) and evidence frameworks is suggested. Experimental results show the effectiveness of the proposed method.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.1-18
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• 2000

In this paper, we study several parameter estimation methods used for autoregressive processes and compare them in view of forecasting. The least square estimation, least absolute deviation estimation, robust estimation are compared through Monte Carlo simulations.

• Journal of Korean Society for Quality Management
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• v.11 no.2
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• pp.18-24
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• 1983

In this paper, subset selection procedures are proposed for selecting the Weibull population with the smallest scale parameter out of k Weibull populations with a common shape parameter. The proposed procedures are based on the maximum likelihood estimators. The constants to implement the procedures are tabulated using Monte Carlo methods. Also, the results of a comparison study are given.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.747-752
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• 2019

By using the failure information and the cumulative test execution time obtained by performing the reliability growth test, it is possible to estimate the parameter of the reliability growth model, and the Mean Time Between Failure (MTBF) of the product can be predicted through the parameter estimation. However the failure information could be acquired periodically or the number of sample data of the obtained failure information could be small. Because there are various constraints such as the cost and time of test or the characteristics of the product. This may cause the error of the parameter estimation of the reliability growth model to increase. In this study, the Bayesian method is applied to estimating the parameters of the reliability growth model when the number of sample data for the fault information is small. Simulation results show that the estimation accuracy of Bayesian method is more accurate than that of Maximum Likelihood Estimation (MLE) respectively in estimation the parameters of the reliability growth model.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.431-440
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• 2013

We consider the problem of estimating the parameters of heavy tailed distributions. In general, maximum likelihood estimation(MLE) is the most preferred method of parameter estimation because it has good properties such as asymptotic consistency, normality and efficiency. However, MLE is not always the best solution because MLE is unstable or does not exist in some cases. This paper proposes another parameter estimation method, non-linear least squares(NLS) and compares its performance to MLE. The NLS estimator is achieved by minimizing sum of squared difference between empirical cumulative distribution function(CDF) and a theoretical distribution function. In this article, we compare the NLS method to MLE using simulated data from heavy tailed distributions. The NLS method is shown to perform better than MLE in Burr distribution when the sample size is small; in addition, it performs well in a Frechet distribution.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.447-460
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• 2006

This paper develops a multiple comparison method for finding an optimal ordering of K geometric means of exponential parameters. This is based on the paired comparison experimental arrangement whose results can naturally be represented by a completely oriented graph. Introducing posterior preference probabilities and stochastic transitivity conditions to the graph, we obtain a new graphical model that yields criteria for the optimal ordering in the multiple comparison. Necessary theories involved in the method and some computational aspects are provided. Some numerical examples are given to illustrate the efficiency of the suggested method.