• Journal of the Korean Statistical Society
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• 제28권3호
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• pp.359-370
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• 1999

We considered a change-point hazard rate model generalizing constant hazard rate model. This type of model is very popular in the sense that the Weibull and exponential distributions formulating survival time data are the special cases of it. Maximum likelihood estimation and the asymptotic properties such as the consistency and its limiting distribution of the change-point estimator were discussed. A parametric bootstrap method for finding confidence intervals of the unknown change-point was also suggested and the proposed method is explained through a practical example.

• 품질경영학회지
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• 제24권1호
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• pp.87-95
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• 1996

We construct bootstrap confidence bounds for reliability, R=P(X>Y), where X and Y are independent normal random variables. 1-way random effect models with equal variances are assumed for the populations of X and Y. We compare the accuracy of the proposed bootstrap confidence bounds and classical confidence bound for small samples via Monte Carlo simulation.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1992년도 춘계공동학술대회 발표논문 및 초록집; 울산대학교, 울산; 01월 02일 May 1992
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• pp.105-116
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• 1992

이 논문에서는 discrete-event모의실험을 사용해서 대기행렬모형에서 대기시간이 길어지는 확률을 추정하는 문제를 연구했습니다. 단 한번의 모의실험에서 확률의 신뢰구간을 구할 수 있는 방법인, binary bootstrap을 개발했습니다. Bernoulli trial과 first-order Markov processes에 적용하여 본 결과 이론치에 별 차이없이 추정하였습니다. 또한 M/M/1 대기행렬모형에서 대기시간이 길 확률을 추정했을 때 batch means방법보다 binary bootstrap이 월등히 우수한 결과를 보였습니다.

• 품질경영학회지
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• 제34권3호
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• pp.62-65
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• 2006

We can use the ridge regression as a means for stabilizing the coefficient estimators in the fitted model when performing experiments in highly constrained regions causes collinearity problems in mixture experiments. But there is no theory available on which to base statistical inference of ridge estimators. The bootstrap could be used to seek the confidence intervals of ridge estimators.

• Journal of the Korean Statistical Society
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• 제36권3호
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• pp.397-410
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• 2007

For the two groups data from multivariate binomial distribution, we consider a bootstrap approach to inferring the simultaneous confidence level and its standard error of a collection of the dependent confidence intervals for the difference of proportions with an experimentwise error rate at the a level are presented. The bootstrap method is used to estimate the simultaneous confidence probability for the difference of proportions.

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.675-686
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• 2009

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation when the training samples include missing values or not. We consider the bootstrap confidence intervals for classification error rate when a block of observation is missing.

• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.205-216
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• 1996

The bootstrap calibration method for empirical likelihood is considered to make a confidence region for the regression coefficients. Asymptotic properties are studied regarding the coverage probability. Small sample simulation results reveal that the bootstrap calibration works quite well.

• Communications for Statistical Applications and Methods
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• 제3권1호
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• pp.87-99
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• 1996

We construct bootstrap confidence intervals for reliability, R= P{X>Y}, where X and Y are independent normal random variables. One way ANOVA random effect models are assumed for the populations of X and Y, where standard deviations $\sigma_{x}$ and $\sigma_{y}$ are unequal. We investigate the accuracy of the proposed bootstrap confidence intervals and classical confidence intervals work better than classical confidence interval for small sample and/or large value of R.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.899-911
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• 2000

In this paper, we reviewed thirteen methods for finding confidence intervals for he mean of poisson distribution. Bootstrap confidence intervals are also introduced. Two bootstrap confidence intervals are compared with the other existing eleven confidence intervals by using Monte Carlo simulation with respect to the average coverage probability of Woodroofe and Jhun (1989).

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.837-843
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• 2000

Kreiss and Franke(192) and Allen and Datta(1999) proposed bootstrapping the M-estimators in ARMA models. In this paper, we introduce the robust estimating function and investigate the bootstrap approximations of the M-estimators which are solutions of the estimating equations in TAR models. A number of simulation results are presented to estimate the sampling distribution of the M-estimators, and asymptotic validity of the bootstrap for the M-estimators is established.