• Journal of the Chungcheong Mathematical Society
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• v.14 no.2
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• pp.47-54
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• 2001

Let {Xn, n = 1,2,${\cdots}$} be i.i.d. random variables with the only unknown parameters mean ${\mu}$ and variance a ${\sigma}^2$. We consider a sequential confidence interval C1 for the mean with coverage probability 1-${\alpha}$ and expected length of confidence interval $E_{\theta}$(Length of CI)/${\mid}{\mu}{\mid}{\leq}k$ (k : constant) and give some asymptotic properties of the stopping time in various limiting situations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.4
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• pp.151-161
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• 1993

The log-Gumbel distribution in real space is defined by transforming the conventional log-Gumbel distribution in log space. For this model, the parameter estimation techniques are applied based on the methods of moments, maximum likelihood and probability weighted moments. The asymptotic variances of estimator of the quantiles for each estimation method are derived to find the confidence limits for a given return period. Finally, the log-Gumbel model is applied to actual flood data to estimate the parameters, quantiles and confidence limits.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.293-301
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• 2014

In this paper, we propose a new measure based on the confidence of Bayesian posterior probability so as to reduce unimportant information in the clustering process. Because the performance of clustering is up to selecting the important degree of attributes within the databases, the concept of information entropy is added to posterior probability for attributes discernibility. Hence, The same value of attributes in the confidence of the proposed measure is considerably much less due to the natural logarithm. Therefore posterior probability-based clustering algorithm selects the minimum of attribute reducts and improves the efficiency of clustering. Analysis of the validation of the proposed algorithms compared with others shows their discernibility as well as ability of clustering to handle uncertainty with ACME categorical data.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.187-201
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• 2008

Earthquake induced hysteretic energy demand for a structure can be used as a limiting value of a certain performance level in seismic design of structures. In cases where it is larger than the hysteretic energy dissipation capacity of the structure, failure will occur. To be able to select the limiting value of hysteretic energy for a particular earthquake hazard level, it is required to define the variation of hysteretic energy in terms of probabilistic terms. This study focuses on the probabilistic evaluation of earthquake induced worst failure probability and approximate confidence intervals for inelastic single-degree-of-freedom (SDOF) systems with a typical steel moment connection based on hysteretic energy. For this purpose, hysteretic energy demand is predicted for a set of SDOF systems subject to an ensemble of moderate and severe EQGMs, while the hysteretic energy dissipation capacity is evaluated through the previously published cyclic test data on full-scale steel beam-to-column connections. The failure probability corresponding to the worst possible case is determined based on the hysteretic energy demand and dissipation capacity. The results show that as the capacity to demand ratio increases, the failure probability decreases dramatically. If this ratio is too small, then the failure is inevitable.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.355-366
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• 2002

Nonparametric statistical inference for the parameter of logit model were examined. Usually nonparametric approach is milder than parametric approach based on normal theory assumption. We compared the two nonparametric methods for legit model, the bootstrap and random permutation in the sense of coverage probability. Monte Carlo simulation is conducted for small sample cases. Empirical power of hypothesis test and coverage probability for confidence interval estimation were presented for simple and multiple legit model respectively. An example were also introduced.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.191-196
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• 2014

In the evaluation of measured noise data, tolerance shall be decided based on the uncertainty. The uncertainty has frequency variations due to the different standard deviations at each frequency. Therefore, tolerance shall be differently decided for each frequency with the same confidence probability. In the report, the evaluation method considering the frequency variation of uncertainty will be introduced. From the approach, considering the actual noise distribution characteristics of the ships, the tolerance shall be decided for each frequency with the same probability, but the overall averaged value shall be kept to the value designated in each notation.

• Journal of Korean Society for Quality Management
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• v.31 no.4
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• pp.164-175
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• 2003

This paper investigates bootstrap confidence intervals of the process capability index(PCI) based on the expected loss derived from the empirical distribution function(EDF). The PCI based on the expected loss is too complex to derive its confidence interval analytically, so the bootstrap method is a good alternative. We propose three types of the bootstrap confidence interval; the standard bootstrap(SB), the percentile bootstrap(PB), and the acceleration biased­corrected percentile bootstrap(ABC). We also perform a comprehensive simulation study under various process distributions, in order to compare the accuracy of the coverage probability of the bootstrap confidence intervals. In most cases, the coverage probabilities of the bootstrap confidence intervals from the EDF PCI turned out to be more accurate than those from the PCI based on the normal distribution. It is expected that the bootstrap confidence intervals from the EDF PCI can be utilized in real processes where the true distribution family may not be known.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.1
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• pp.80-86
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• 2007

The concept of the coverage has been played an important role in the area of reliability evaluation of a system. The widely used measures of reliability include the m time between failures, the availability and so on. In this paper, we propose an estimator of the coverage probability in a redundant system with a control unit and investigate some moments of the proposed estimator. And assuming exponential distribution of all units, we conduct a simulation study for calculating the estimates of the coverage probability and its confidence bounds. An example of evaluating the availability of an optical transportation system is illustrated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.193-197
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• 2016

The performance of Precision Guided Missiles is estimated by using hit probability only, which is calculated by hits against total amounts of fires in current domestic live-fire tests. It has a limitation in judging the performance of all produced Precision Guided Missiles by using the calculated hit probability according to the result of live-fire test, because the overall characteristics of the produced Precision Guided Missiles are not considered. In other words, a method is needed to estimate the confidence level which is more reliable than simply calculated hit probability according to the result of live-fire test for guaranteeing the hit probability of Precision Guided Missiles by certain level, which is already being operated or produced. This paper introduces a method to estimate the confidence level of Precision Guided Missiles by minimum live-fire tests using Hypergeometric distribution and Bayes' rule suitable for the characteristics of Precision Guided Missiles, which are small production, high costs and unable to check whether the missile hits the target or not before the live-fire tests. Also, this paper suggests a reasonable confidence level for showing the performance of the Precision Guided Missiles using the results of live-fire tests and domestic and foreign literature, when the result of live-fire tests will be decided.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.501-509
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• 2009

Recently the interval estimation of binomial proportions is revisited in various literatures. This is mainly due to the erratic behavior of the coverage probability of the well-known Wald confidence interval. Various alternatives have been proposed. Among them, the Agresti-Coull confidence interval, the Wilson confidence interval and the Bayes confidence interval resulting from the noninformative Jefferys prior were recommended by Brown et al. (2001). However, unlike the binomial distribution case, little is known about the properties of the confidence intervals in finite population sampling. In this note, the property of confidence intervals is investigated in anile population sampling.