• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.35-46
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• 2019

Multivariate Confidence Region (MCR) cannot be used to obtain the confidence region of the mean vector of multivariate data when the normality assumption is not satisfied; however, the Quantile Confidence Region (QCR) could be used with a Multivariate Quantile Vector in these cases. The coverage rate of the QCR is better than MCR; however, it has a disadvantage because the QCR has a wide shape when the probability density function follows a bimodal form. In this study, we propose a Quantile Confidence Region using the Highest density (QCRHD) method with the Highest Density Region (HDR). The coverage rate of QCRHD was superior to MCR, but is found to be similar to QCR. The QCRHD is constructed as one region similar to QCR when the distance of the mean vector is close. When the distance of the mean vector is far, the QCR has one wide region, but the QCRHD has two smaller regions. Based on these features, it is found that the QCRHD can overcome the disadvantages of the QCR, which may have a wide shape.

• 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 the Korean Statistical Society
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• v.28 no.3
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• pp.297-309
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• 1999

A sequential procedure of fixed-width confidence intervals for quantiles satisfying a condition of coverage probability is provided based on recursive density estimators. It is shown that the proposed sequential procedure is asymptotically efficient. In addition, the asymptotic normality for the proposed stopping time is derived.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.263-269
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• 1996

The somplest approximate confidence interval for the probability of success is the one based on the normal approximation to the binomial distribution, It is widely used in the introductory teaching, and various guidelines for its use with "large" sample have appeared in the literature. This paper suggests a guideline when to use it as an approximation to the exact confidence interval, and comparisons with existing guidelines are provided. provided.

• Journal of the Korean Statistical Society
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• v.18 no.1
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• pp.26-37
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• 1989

When we select the t best out of k populations in the indifference zone formulation, a lower confidence bound on the probability of a correct selection is derived for families with monotone likelihood ratio. The result is applied to the normal means problem when the variance is common, and to the normal variances problem. Tables to implement the confidence bound for the normal variances problem are provided.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.205-210
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• 2004

A weighted self-tuning robust regression estimator (WSTE) has the high breakdown point for estimating regression parameters such as other well known high breakdown estimators. In this paper, we propose to obtain standard quantities like confidence intervals, and it is found to be superior to the other high breakdown regression estimators when a sample is contaminated

• Communications for Statistical Applications and Methods
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• v.4 no.2
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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 Statistical Society
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• v.27 no.4
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• pp.435-449
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• 1998

In this paper, we consider the problem of constructing the lower cofidence intervals for the reliability P(X < Y z,w), where the stress X and the strength Y are the random variables with explanatory variables z and w, respectively. As an estimator of the reliability, a Mann-Whitney type statistic is considered. It is shown that under regularity conditions, the proposed estimator is asymptotically normal. Based on the result, the distribution free lower confidence intervals are constructed.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.311-315
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• 1997

In this note we consider confidence interval based on Kolmogorov-Smirnov statistic. In order to obtain confidence interval we need percentage points of the statistics. Bootstrap method is examined whether it is useful to determine the points. It is concluded that the method is useful for observations with many ties, whereas it gives less conserbative points for continuous distributions.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.75-78
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• 2003

Those who are interested in making inferences concerning linear combination of variance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods.