• 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.

• Journal of the Korean Statistical Society
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• v.22 no.1
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• pp.39-54
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• 1993

In the three-factor nested variance component model with equal numbers in the cells given by $y_{ijkm} = \mu + A_i + B_{ij} + C_{ijk} + \varepsilon_{ijkm}$, the exact confidence intervals of the variance component of $\sigma^2_A, \sigma^2_B, \sigma^2_C, \sigma^2_{\varepsilon}, \sigma^2_A/\sigma^2_{\varepsilon}, \sigma^2_B/\sigma^2_{\varepsilon}, \sigma^2_C/\sigma^2_{\varepsilon}, \sigma^2_A/\sigma^2_C, \sigma^2_B/\sigma^2_C$ and $\sigma^2_A/\sigma^2_B$ are not found out yet. In this paper approximate lower and upper confidence intervals are presented.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.691-699
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• 2017

Data analysts sometimes test the equality of two normal population means by the inspection of the overlapping of two confidence intervals. This method seems simple to use; however, it is a common statistical misconception to suppose that two normal means are not significantly different because of no overlapping. This article will present transforming the confidence interval of the mean difference to individual confidence intervals that are visualized to inspect overlapping. It will also be shown that this technique can be extended when comparing the k normal population means with equal variances.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.731-743
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• 2009

The interval estimation for binomial proportion has been treated practically as well as theoretically for a long time. In this paper we compared the properties of major confidence intervals and summarized current issues for coverage probability and interval length which are the criteria of evaluation for confidence interval. Additionally, we examined the three topics which were considered in using the binomial confidence interval in the field. And finally we discussed the future studies for a low binomial proportion.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.281-289
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• 2002

In this paper, we considered an application of the bootstrap method for logit model. Estimation of type I error probability, the bootstrap p-values and bootstrap confidence intervals of parameter were proposed. Small sample Monte Carlo simulation were conducted in order to compare proposed method with existing normal theory based asymptotic method.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.179-187
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• 2013

Bates and Watts (1981) have discussed the problems of reparameterizing nonlinear models in obtaining accurate linear approximation confidence regions for the parameters. A similar problem exists with computing confidence curves for fitted values or predictions. The statistical behavior of fitted values does not depend on the parameterization. Thus, as long as the intrinsic curvature is small, standard Wald intervals for fitted values are likely to be sufficient. Accuracy of linear approximation for fitted values is investigated using confidence curves.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.389-399
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• 2002

In order to examine whether the difference between two point estimates of population proportions is statistically significant, data analysts use two techniques. The first is to explore the overlap between two associated confidence intervals. Second method is to test the significance which is introduced at most statistical textbooks under the common assumptions of consistency, asymptotic normality, and asymptotic independence of the estimates. Under the null hypothesis which is two population proportions are equal, the pooled estimator of population proportion is preferred as a point estimator since two independent random samples are considered to be collected from one population. Hence as an alternative method, we could obtain another confidence interval of the difference of the population proportions with using the pooled estimate. We conclude that, among three methods, the overlapped method is under-estimated, and the difference of the population proportions method is over-estimated on the basis of the proposed method.

• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.217-223
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• 2010

The research proposes the complementary methodology using integrated hypothesis testing and confidence interval models that can be identified the statistical difference and practical equivalence. The models developed in this study can be used in the quality improvement processes such as QC story 15 steps. For the expressions of CI4LSD(Confidence Interval for Least Significant Difference) and CI4TOST(Confidence Interval for Two One-Sided Tests) are simple, quality practioners can efficiently handle them. CI4TOST models as a complement can be applied when CI4LSD models are influenced by sample size and precision.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.227-235
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• 1993

Calibration relates the estimation of independent variable which rquires more effort or expense than dependent variable does. It would be provided with high accuracy because a little change of the result of independent variable cn cause a serious effect to the human being. Usual statistical analysis assumes the normality of error distribution or linearity of data. It is desirable to analyze the data without those assumptions for the accuracy of the calibration. In this paper, we calibrated the data nonparametrically without those assumptions and derived confidence interval estimate for the independent variable. As a method, we used kernel method which is popular in modern statistical branch. We derived bootstrap confidence interval estimate from the bootstrap confidence band.