• Proceedings of the Korea Society for Simulation Conference
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• 2001.05a
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• pp.169-169
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• 2001

With the aid of fast computing power, resampling techniques are being introduced for simulation output analysis (SOA). Autocorrelation among the output from discrete-event simulation prohibit the direct application of resampling schemes (Threshold bootstrap, Binary bootstrap, Stationary bootstrap, etc) extend its usage to time-series data such as simulation output. We present a new method for inference from a regenerative process, regenerative bootstrap, that equals or exceeds the performance of classical regenerative method and approximation regeneration techniques. Regenerative bootstrap saves computation time and overcomes the problem of scarce regeneration cycles. Computational results are provided using M/M/1 model.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.321-327
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• 2011

Bootstrap methods is the computer-based resampling method that estimates the standard errors and confidence intervals of summary statistics using the plug-in principle for assessing the accuracy or uncertainty of statistical estimates, and the BCa method among the Bootstrap methods is known much superior to other Bootstrap methods in respect of the standards of statistical validation. Therefore this study suggests the method of the representation and treatment of uncertainty in flood risk assessment and water resources planning from the construction and application of rainfall frequency analysis model considersing the uncertainty based on the nonparametric BCa method among the Bootstrap methods for the assessement of the estimation of probability rainfall and the effect of uncertainty considering the uncertainty of the parameter estimation of probability in the rainfall frequency analysis that is the most fundamental in flood risk assessement and water resources planning.

• Journal of the Korea Society for Simulation
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• v.19 no.3
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• pp.119-125
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• 2010

The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are methods of simulation output analysis, which are applicable to autocorrelated data. These bootstrap methods assume the stationarity of data. However, bootstrap methods cannot work if the stationary assumption is not guaranteed because of seasonality or trends in data. In the simulation output analysis, threshold bootstrap method is the best in describing the autocorrelation structure of original data set. The threshold bootstrap makes the cycle based on threshold value. If we apply the bootstrap to seasonality data, we can get similar accuracy of the results. In this paper, we verify the possibility of applying the bootstrap to seasonal data.

• International Journal of Quality Innovation
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• v.2 no.2
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• pp.34-45
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• 2001

This study uses MATLAB as a programming tool and applies the bootstrap method to process capability analysis. The advantage of using MATLAB in bootstrap method is to make the bootstrap method much easier to implement and apply particularly in process capability analysis. An example is provided to further illustrate the easy use of MATLAB in bootstrap method.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.65-73
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• 2013

In the simulation output analysis, bootstrap method is an applicable resampling technique to insufficient data which are not significant statistically. The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are typical bootstrap methods to be used for autocorrelated time series data. They are nonparametric methods for stationary time series data, which correctly describe the original data. In the simulation output analysis, however, we may not use them because of the non-stationarity in the data set caused by the trend such as increasing or decreasing. In these cases, we can get rid of the trend by differencing the data, which guarantees the stationarity. We can get the bootstrapped data from the differenced stationary data. Taking a reverse transform to the bootstrapped data, finally, we get the pseudo-samples for the original data. In this paper, we introduce the applicability of bootstrap methods to the time series data having trend, and then verify it through the statistical analyses.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.755-767
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• 1997

Analyzing autocorrelated data set is still an open problem. Developing on easy and efficient method for severe positive correlated data set, which is common in simulation output, is vital for the simulation society. Bootstrap is on easy and powerful tool for constructing non-parametric inferential procedures in modern statistical data analysis. Conventional bootstrap algorithm requires iid assumption in the original data set. Proper choice of resampling units for generating replicates has much to do with the structure of the original data set, iid data or autocorrelated. In this paper, a new bootstrap resampling scheme is proposed to analyze the autocorrelated data set : the Threshold Bootstrap. A thorough literature search of bootstrap method focusing on the case of autocorrelated data set is also provided. Theoretical foundations of Threshold Bootstrap is studied and compared with other leading bootstrap sampling techniques for autocorrelated data sets. The performance of TB is reported using M/M/1 queueing model, else the comparison of other resampling techniques of ARMA data set is also reported.

• Korean Journal of Veterinary Research
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• v.47 no.2
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• pp.259-263
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• 2007

The choice of input distribution in quantitative risk assessments modeling is of great importance to get unbiased overall estimates, although it is difficult to characterize them in situations where data available are too sparse or small. The present study is particularly concerned with accommodation of uncertainties commonly encountered in the practice of modeling. The authors applied parametric and non-parametric bootstrap simulation methods which consist of re-sampling with replacement, in together with the classical Student-t statistics based on the normal distribution. The implications of these methods were demonstrated through an empirical analysis of trade volume from the amount of chicken and pork meat imported to Korea during the period of 1998-2005. The results of bootstrap method were comparable to the classical techniques, indicating that bootstrap can be an alternative approach in a specific context of trade volume. We also illustrated on what extent the bias corrected and accelerated non-parametric bootstrap method produces different estimate of interest, as compared by non-parametric bootstrap method.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1406-1411
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• 2009

Bootstrap 기법은 통계학적 추정치의 정확도 또는 불확실성을 평가하기 위한 컴퓨터 기반 리샘플링 기법으로서 플러그인 원칙을 이용하여 요약통계치의 표준오차 및 신뢰구간을 추정하며, Bootstrap 기법 중 BCa 기법은 다른 Bootstrap 기법들에 비해 적합도 기준면에서 훨씬 우수한 결과를 나타내는 것으로 알려져 있다. 본 논문에서는 강우빈도해석에서 확률분포의 매개변수 추정에 대한 불확실성 고려한 확률강우량의 산정 및 불확실성의 영향을 평가하기 위하여 Bootstrap 기법 중 비매개변수적 BCa 기법에 기반한 불확실성을 고려한 강우빈도해석모델 구축 및 적용을 통해 홍수위험평가 및 수자원 계획 등에 있어서 불확실성 표현 및 처리기법을 제시하였다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.37-43
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• 2007

ANOVA is widely used for measurement system analysis. It assumes that the measurement error is normally distributed, which may not be seen in certain industrial cases. In this study, the exact and bootstrap confidence intervals for precision-to-tolerance ratio (PTR) are obtained for the cases where the measurement errors are normally and non-normally distributed and the reproducibility variation can be ignored. Lognormal and gamma distributions are considered for non-normal measurement errors. It is assumed that the quality characteristics have the same distributions of the measurement errors. Three different bootstrap methods of SB (Standard Bootstrap), PB (Percentile Bootstrap), and BCPB (Biased-Corrected Percentile Bootstrap) are used to obtain bootstrap confidence intervals for PTR. Based on a coverage proportion of PTR, a comparative study of exact and bootstrap methods is performed. Simulation results show that, for non-normal measurement error cases, the bootstrap methods of SB and BCPB are superior to the exact one.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.367-377
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• 2010

In this study, we considered annual maximum rainfall data from 56 weather stations for rainfall frequency analysis using SIR(Sampling Important Resampling) algorithm and Bootstrap method. SIR algorithm is resampling method considering weight in extreme rainfall sample and Bootstrap method is resampling method without considering weight in rainfall sample. Therefore we can consider the difference between SIR and Bootstrap method may be due to the climate change. After the frequency analysis, we compared the results. Then we derived the results which the frequency based rainfall obtained using the data from SIR algorithm has the values of -10%~60% of the rainfall obtained using the data from Bootstrap method.