• 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.27 no.6
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• pp.867-889
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• 2014

Nonparametric Bayesian (np Bayes) statistical models are popularly used in a variety of research areas because of their flexibility and computational convenience. This paper reviews the np Bayes models focusing on biomedical research applications. We review key probability models for np Bayes inference while illustrating how each of the models is used to answer different types of research questions using biomedical examples. The examples are chosen to highlight the problems that are challenging for standard parametric inference but can be solved using nonparametric inference. We discuss np Bayes inference in four topics: (1) density estimation, (2) clustering, (3) random effects distribution, and (4) regression.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.55-63
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• 2019

In engineering problems, many random variables have correlation, and the correlation of input random variables has a great influence on reliability analysis results of the mechanical systems. However, correlated variables are often treated as independent variables or modeled by specific parametric joint distributions due to difficulty in modeling joint distributions. Especially, when there are insufficient correlated data, it becomes more difficult to correctly model the joint distribution. In this study, multivariate kernel density estimation with bounded data is proposed to estimate various types of joint distributions with highly nonlinearity. Since it combines given data with bounded data, which are generated from confidence intervals of uniform distribution parameters for given data, it is less sensitive to data quality and number of data. Thus, it yields conservative statistical modeling and reliability analysis results, and its performance is verified through statistical simulation and engineering examples.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.4-10
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• 1995

이동가입자 수신단에서의 평균 수신전력 레벨이 핸드오버 임계값과 수신기 임계값 사이에 있는 영역을 핸드오버 영역이라고 하며, 이동가입자가 핸드오버 영역이라고 하며, 이동가입자가 핸드오버 영역에 머무르는 시간을 핸드오버 지속시간(Handover Duration Time)으로 정의한다. 본 논문에서는 이동통신 시스템에서 트래픽 모델링시 중요한 파라메타중 하나인 핸드오버 지속시간에 대한 분포를 추정한다. 첫번째로 핸드오버 지속시간의 분포군을 선택하기 위해 시뮬레이션 결과로부터 얻어진 샘플 데이타를 이용하여 점 통계량, 히스토그램, 확률도의 방법을 적용하며, 두번째로 구체적인 분포를 결정하기 위해서 모수(parameter)의 값들을 추정하는데, 본 논문에서는 모수를 추정하기 위해서 최우추정량을 사용하여 모수의 값들을 산출하고 이를 토대로 적합도 검정을 수행한다. 최종적인 분석 결과 핸드오버 지속시간은 감마 분포를 따르는 것을 제시하였다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.153-161
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• 2017

Branching processes which is used for epidemic dispersion as stochastic process model have advantages to estimate parameters by real data. We have to estimate both mean and dispersion parameter in order to use the negative binomial distribution as an offspring distribution on branching processes. In existing studies on biology and epidemiology, it is estimated using maximum-likelihood methods. However, for most of epidemic data, it is hard to get the best precision of maximum-likelihood estimator. We suggest a Bayesian inference that have good properties of statistics for small-sample. After estimating dispersion parameter we modelled the posterior distribution for 2015 Korea MERS cases. As the result, we found that the estimated dispersion parameter is relatively stable no matter how we assume prior distribution. We also computed extinction probabilities on branching processes using estimated dispersion parameters.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.647-663
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• 2017

Weibull distribution is a popular distribution for modeling lifetimes because it reflects the characteristics of failure adequately and it models either increasing or decreasing failure rates simply. It is a standard method of the lifetimes test to wait until all samples failed; however, censoring can occur due to some realistic limitations. In this paper, we propose a generalized likelihood ratio (GLR) chart to monitor changes in the scale parameter for type I right-censored Weibull lifetime data. We also compare the performance of the proposed GLR chart with two CUSUM charts proposed earlier using average run length (ARL). Simulation results show that the Weibull GLR chart is effective to detect a wide range of shift sizes when the shape parameter and sample size are large and the censoring rate is not too high.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.85-100
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• 2023

The stochastic volatility (SV) model is one of the main methods of modeling time-varying volatility. In particular, SV model is actively used in estimation and prediction of financial market volatility and option pricing. This paper attempts to model the time-varying volatility of the bitcoin market price using SV model. Hidden Markov model (HMM) is combined with the SV model to capture characteristics of regime switching of the market. The HMM is useful for recognizing patterns of time series to divide the regime of market volatility. This study estimated the volatility of bitcoin by using data from Upbit, a cryptocurrency trading site, and analyzed it by dividing the volatility regime of the market to improve the performance of the SV model. The MCMC technique is used to estimate the parameters of the SV model, and the performance of the model is verified through evaluation criteria such as MAPE and MSE.

• Journal of Digital Contents Society
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• v.8 no.1
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• pp.77-83
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• 2007

The handoff area is the region where a call can be handled by the base station in any of the adjacent cells, and duration time is viewed as the time a mobile station resides in the handoff area. In this paper, probability distribution about hand off time one of important parameter at traffic modelling in mobile communication system was estimated. First, point statistic is applied using sample data obtained from simulation result to choose the group of distribution of handoff duration time. Second, parameters are estimated to decide specific distribution function. For this, the value of parameters is calculated using MLE(Maximum Likelihood Estimate.) and goodness of fit test is performed. finally these results show that handoff duration time follows gamma distribution.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.413-413
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• 2019

하천과 호수에서 남조류의 이상 과잉증식 문제(이하 녹조문제)는 담수생태계의 생물다양성을 감소시키며, 음용수의 이취미 원인물질을 발생시켜 물 이용에 장해가 된다. 또한 독소를 생산하는 유해남조류가 대량 증식할 경우에는 가축이나 인간의 건강에 치명적 해를 끼치기도 한다. 그 동안 국내에서 녹조문제는 댐 저수지와 하구호와 같은 정체수역에서 간헐적으로 문제를 일으켰으나, 4대강사업(2010-2011)으로 16개의 보가 설치된 이후 낙동강, 금강, 영산강 등 대하천에서도 광범위하게 발생되고 있어 중요한 사회적 환경적 이슈로 대두되었다. 한편, 대하천에 설치된 보 구간에서 빈번히 발생하는 녹조현상의 원인에 대해서는 전 지구적 기온상승에 따른 기후변화의 영향이라는 주장과 유역으로부터 영양염류의 과도한 유입, 가뭄에 따른 유량감소, 보 설치에 따른 체류시간 증가 등 다양한 의견이 제시되고 있으나, 대상 유역과 수체의 특성에 따라 녹조 발생의 원인이 상이하거나 또는 다양한 요인이 복합적으로 작용하기 때문에 보편적 해석(universal interpretation)이 어려운 것이 현실이다. 따라서 각 수계별, 보별 녹조현상에 대한 정확한 원인분석과 효과적인 대책 마련을 위해서는 집중된 실험자료와 데이터마이닝 기법에 근거로 한 보다 과학적이고 객관적인 접근이 이루어져야 한다. 본 연구에서는 2012년 보 설치 이후 남조류에 의한 녹조현상이 빈번히 발생하고 있는 낙동강 4개보(강정고령보, 달성보, 합천창녕보, 창녕함안보)를 대상으로 집중적인 현장조사와 실험분석을 수행하고, 수집된 기상, 수문, 수질, 조류 자료에 대해 통계분석과 다양한 데이터모델링 기법을 적용하여 보별 남조류 우점 환경조건과 이를 제어하기 위한 주요 조절변수를 규명하는데 있다. 연구대상 보 별 수질과 식물플랑크톤의 정성 및 정량 실험은 2017년 5월부터 2018년 11월까지 2년에 걸쳐 실시하였으며, 남조류 세포수 밀도와 환경요인과의 상관성 분석을 실시하고, 단계적 다중회귀모델(Step-wise Multiple Linear Regressions, SMLR), 랜덤포레스트(Random Forests, RF) 모델과 재귀적 변수 제거 기법(Recursive Feature Elimination using Random Forest, RFE-RF)을 이용한 변수중요도 평가, 의사결정나무(Decision Tree, DT), 주성분분석(Principal Component Analysis, PCA) 기법 등 다양한 모수적 및 비모수적 데이터마이닝 결과를 바탕으로 각 보별 남 조류 우점 환경요인을 종합적으로 해석하였다.