• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.357-363
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• 1998

In this paper we propose a nonparametric lack of fit test based on the bootstrap method for testing the null parametric linear model by using linear smoothers. Most of existing nonparametric test statistics are based on the residuals. Our test is based on the centered bootstrap residuals. Power performance of proposed bootstrap lack of fit test is investigated via Monte carlo simulation.

• Communications for Statistical Applications and Methods
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• 제4권2호
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• pp.523-532
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• 1997

We propose several estimators of the reliability function R of the two-parameter exponential distribution, and then compare those estimator in terms of the mean square error (MSE) through Monte Carlo method. We also consider the parametric bootstrap estimation. Using the parametric bootstrap estimator, we obtain the bootstrap confidence intervals for reliability function and compare the proposed bootstrap confidence intervals in terms of the length and the coverage probability through Monte Carlo method.

• Journal of the Korean Data and Information Science Society
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• 제8권1호
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• pp.59-70
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• 1997

We introduce several estimators of the location and the scale parameters of the two-parameter exponential distribution, and then compare these estimators by the mean square error (MSE). Using the parametric bootstrap estimators and the delete-d jackknife, we obtain the bootstrap and the delete-d jackknife confidence intervals for the location and the scale parameters and compare the bootstrap confidence intervals with the delete-d jackknife confidence intervals by length and coverage probability through Monte Carlo method.

• International Journal of Quality Innovation
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• 제2권2호
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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 Data and Information Science Society
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• 제10권2호
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• pp.465-472
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• 1999

We considered a bootstrap method for constructing confidenc intervals for a one parameter model using multinomial sampling. The convergence rates or the proposed bootstrap method are calculated for model-based maximum likelihood estimators(MLE) using multinomial sampling. Monte Carlo simulation was used to compare the performance of bootstrap methods with normal approximations in terms of the average coverage probability criterion.

• Communications for Statistical Applications and Methods
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• 제23권6호
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• pp.587-592
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• 2016

It is a long-standing issue to correctly determine the number of long-run relationships among time series processes. We revisit nonparametric test for cointegration rank and propose bootstrap refinements. Consistent with model-free nature of the tests, we make use of Cholesky factor bootstrap methods, which require weak conditions for data generating processes. Simulation studies show that the original Breitung's test have difficulty in obtaining the correct size due to dependence in cointegrated errors. Our proposed bootstrapped tests considerably mitigate size distortions and represent a complementary approach to other bootstrap refinements, including sieve methods.

• Journal of the Korean Statistical Society
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• 제26권3호
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• pp.383-395
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• 1997

This paper proves that the standard bootstrap approximation for the least absolute deviation (LAD) estimate of .beta. in AR(1) processes with infinite variance error terms is asymptotically valid in probability when the bootstrap resample size is much smaller than the original sample size. The theoretical validity results are supported by simulation studies.

• Journal of the Korean Statistical Society
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• 제32권3호
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• pp.213-224
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• 2003

We give an overview of results which have appeared or will appear elsewhere demonstrating that by suitably modifying the bootstrap principle, its applicability can be greatly enhanced. Although we state our results for the iid case, extensions are, at least heuristically, easy.

• 경영과학
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• 제22권1호
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• pp.15-26
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• 2005

The bootstrap is a method of computational inference that simulates the creation of new data by resampling from a single data set. We propose a new job for the bootstrap: generating inputs from one historical trace using Threshold Bootstrap. In this regard, the most important quality of bootstrap samples is that they be functionally indistinguishable from independent samples of the same stochastic process. We describe a quantitative measure of difference between two time series, and demonstrate the sensitivity of this measure for discriminating between two data generating processes. Utilizing this distance measure for the task of generating inputs, we show a way of tuning the bootstrap using a single observed trace. This application of the threshold bootstrap will be a powerful tool for Monte Carlo simulation. Monte Carlo simulation analysis relies on built-in input generators. These generators make unrealistic assumptions about independence and marginal distributions. The alternative source of inputs, historical trace data, though realistic by definition, provides only a single input stream for simulation. One benefit of our method would be expanding the number of inputs achieving reality by driving system models with actual historical input series. Another benefit might be the automatic generation of lifelike scenarios for the field of finance.

• 대한토목학회논문집
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• 제30권4B호
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• pp.367-377
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• 2010

본 연구에서는 기상청 산한 56개 기상관측소의 연최대치계열 일 강우자료를 대상으로 Bootstrap기법과 SIR알고리즘을 이용하여 표본을 재추출한 후, 빈도해석을 적용하여 결과를 비교검토 하였다. SIR알고리즘은 기존에 발생되었던 극한 사상에 가중치를 두어 표본을 재추출하는 방법으로 과거에 발생한 극한사상이 기후변화에 의해서 더욱 빈발하게될 것 이라는 가정에 기초한다고 할 수 있다. 반면에 Bootstrap기법은 현재 발생한 사상에 동일한 가중치를 두어 표본을 재추출하는 방법이다. 따라서 두 방법의 차이를 계산하여 기후변화로 인한 극한강우의 빈도별 확률강우량의 변화를 산정할 수 있다. 비교결과 SIR알고리즘에 의하여 재추출된 강우를 이용하여 산정된 확률강우량의 경우, Bootstrap기법에 의해서 재추출된 강우를 이용하여 산정한 확률강우량에 비해 지점에 따라 작게는 -10%정도의 감소와 크게는 60%정도의 차이를 보임을 확인하였다.