• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.87-94
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• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.405-414
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• 1999

양쪽중단(doubly censored)된 지수분포에서 모수와 신뢰도함수를 계층적 베이지안(hierarchical Bayesian)방법을 이용하여 추정하였다. 베이즈 계산은 깁스표본기법(Gibbs sampler)을 이용하고 또한 완전조건부 분포(full conditional distribution)의 정량화 상수를 모르는 경우에는 적합기각방법(adaptive rejection sampling)을 이용하였다. 그리고 실제자료를 이용하여 분석을 하였다.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.333-339
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• 2013

This paper discusses a method for getting a basis set of estimable functions of less than full rank linear model. Since model parameters are not estimable estimable functions should be identified for making inferences proper about them. So, it suggests a method of using full rank factorization of model matrix to find estimable functions in easy way. Although they might be obtained in many different ways of using model matrix, the suggested full rank factorization technique could be one of much easier methods. It also discusses how to use projection matrix to identify estimable functions.

• Environmental and Resource Economics Review
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• v.22 no.2
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• pp.281-307
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• 2013

There has been some debates about zero willingness to pay in contingent valuation method research. Therefore, this paper tries to estimate and compare the results of various models to handle zero willingness to pay responses. For this purpose, we have employed parametric estimation such as the mixed model and the spike model, as well as non-parametric estimations. As a result, these models derived WTP estimate different from conventional model, but they also show some weakness. Therefore, in future research, more conservative estimate of the model should be to use rather than specific model.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.05a
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• pp.185-190
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• 1999

비모수 회귀모형에 있어서의 오차분산을 추정하는 방법들 중 차분에 기저한 방법(difference-based methods)을 이용한 기존의 추정량들을 비교 분석하는데 목적이 있다. 특히 점근적인 최적 이차차분에 기저한 Hall과 Kay, Titterington(1990)의 HKT 추정량에 대한 그들의 추정량에 대한 문제점들을 제시하고, HKT추정량과, GSJS 추정량, Rice 추정량에 대하여 모의실험을 이용하여 모수에 대한 수렴속도를 비교 분석하였다. 또한 GSJS 추정량에 대한 일치성과 수렴 속도를 보였다.

• 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.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.129-142
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• 2006

본 논문은 공간시계열자료가 공간의 위치와 시간의 흐름에 따라 동시에 관측되는 분야인 기상, 지질, 천문, 생태, 역학 등에서 아주 넓이 사용되고 있고 그 수요가 점차 증가하는 이 시기에 복잡한 공간시계열 중선형(STBL) 모형에 대한 모수 추정 방법 중 수치 해석적 방법인 Newton-Raphson 방법과 Kalman-Filter 방법을 비교하고, 두 가지 방법에 의한 예측력을 비교하여 보았다.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1253-1262
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• 2016

When there exists a change-point in hazard function, it should be estimated for exact parameter or hazard estimation. In this research, we compare the hazard change-point estimators. Matthews and Farewell (1982) parametric change-point estimator is based on the likelihood and Zhang et al. (2014) nonparametric estimator is based on the Nelson-Aalen cumulative hazard estimator. Simulation study is done for the data from exponential distribution with one hazard change-point. The simulated data generated without censoring and the data with right censoring are considered. As real data applications, the change-point estimates are computed for leukemia data and primary biliary cirrhosis data.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.283-292
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• 2008

Smoothing parameter or bandwidth plays a key role in nonparametric classification based on kernel density estimation. We consider choosing smoothing parameter in nonparametric classification, which optimize the Bayes risk. Hall and Kang (2005) clarified the theoretical properties of smoothing parameter in terms of minimizing Bayes risk and derived the optimal order of it. Bootstrap method was used in their exploring numerical properties. We compare cross-validation and bootstrap method numerically in terms of optimal order of bandwidth. Effects on misclassification rate are also examined. We confirm that bootstrap method is superior to cross-validation in both cases.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.295-300
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• 2003

공간적으로 영향을 받는 위치에서의 상호 연관성을 고려한 예측모형 중에서 크리깅 (kriging) 방법은 관측된 데이터를 보간(interpolation)하고, 부드럽게 연결(smoothing)하며, 새로운 데이터를 예측(prediction)하는 통계적 모형으로서 많이 활용되고 있다. 크리깅 모형을 적용하기 위해서는 먼저 주어진 두 위치에서의 비연관성을 나타내는 세미베리오그램 (semivariogram)의 3가지 모수(nugget, sill, range)를 추정해야 한다. 본 연구에서는 전역 적 최적화 방법인 유전자 알고리즘(genetic algorithm)을 도입하여 세미베리오그램 모수들을 추정하였고, 이를 통해 강우량(rainfall)에 대한 크리깅 추정량을 산출하고 효과성을 판단하였다.