• Communications for Statistical Applications and Methods
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• v.7 no.3
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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).

• 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.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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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.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.355-363
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• 2002

Using the Buckley-James method, we construct bootstrap confidence intervals for the regression coefficients under the censored data. And we compare these confidence intervals in terms of the coverage probabilities and the expected confidence interval lengths through Monte Carlo simulation.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.159-169
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• 1993

In this article, we consider two sample problem with randomly right censored data. We propse two-sample confidence intervals for the difference in medians or any quantiles, based on bootstrap. The bootstrap version of two-sample confidence intervals proposed in this article is simple to apply and do not need the assumption of the shift model, so that for the non-shift model, the density estimation is not necessary, which is an attractive feature in small to moderate sized sample case.

• Journal of Korean Society for Quality Management
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• v.23 no.4
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• pp.64-73
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• 1995

In this paper, the stress strength model is assumed for the populations of X and Y, where distributions of X and Y are independent normal with unknown parameters. We construct bootstrap confidence intervals for reliability, R=P(X>Y) and compare the accuracy of the proposed bootstrap confidence intervals and classical confidence interval through Monte Carlo simulation.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.493-502
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• 2010

For establishing forecasting confidence interval for exchange rate, it is critical to estimate distribution of the exchange rate properly. In this thesis, we use block bootstrap method to estimate the distribution of the exchange rate via sum of its daily ratios. As a result, an easier and more accurate forecasting method is provided.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.757-764
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• 2009

In discriminant analysis, we consider a special pattern which contains a block of missing observations. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider the bootstrap confidence intervals of the error rate in the circular models when the covariance matrices are equal and not equal.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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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.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.717-732
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• 2011

We consider bootstrap confidence intervals for high quantiles of heavy-tailed distribution. A semi-parametric method is compared with the non-parametric and the parametric method through simulation study.