• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.825-833
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• 2013

An estimation of confidence intervals is essential to calculate uncertainty for greenhouse gases inventory. It is generally assumed that the population has a normal distribution for the confidence interval of parameters. However, in case data distribution is asymmetric, like nonnormal distribution or positively skewness distribution, the traditional estimation method of confidence intervals is not adequate. This study compares two estimation methods of confidence interval; parametric and non-parametric method for exponential distribution as an asymmetric distribution. In simulation study, coverage probability, confidence interval length, and relative bias for the evaluation of the computed confidence intervals. As a result, the chi-square method and the standardized t-bootstrap method are better methods in parametric methods and non-parametric methods respectively.

• Journal of Korean Society on Water Environment
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• v.26 no.2
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• pp.297-302
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• 2010

It is very important to know mean and confidence interval of water-quality constituents such as BOD for water-quality control and management of rivers and reservoirs effectively. The mean and confidence interval of BOD at Anseong2 and Hwangguji3 sampling stations which are located at the border of local governments in Anseong Stream were estimated and analyzed in this paper using Bootstrap technique which is one of non-parametric statistics. The results of Bootstrap were compared with arithmetic mean, geometric mean, Biweight method mean as a point estimator and distribution mean came from the appropriate probability distribution of Log-normal. In Bootstrap technique 12 data set was randomly selected in each year and 1000 samples was produced to get parameter of population. Visual Basic for Applications (VBA) of Microsoft Excel was utilized in Bootstrap. It was revealed that the Bootstrap technique can be used to explain more rigorously and robustly the achievement or violation of BOD target concentration in Total Maximum Daily Load (TMDL).

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.217-230
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• 2006

e discuss proper confidence intervals for interval estimation of a low binomial proportion. A large sample surveys are practically executed to find rates of rare diseases, specified industrial disaster, and parasitic infection. Under the conditions of 0 < p ${\leq}$ 0.1 and large n, we compared 6 confidence intervals with mean coverage probability, root mean square error and mean expected widths to search a good one for interval estimation of population proportion p. As a result of comparisons, Mid-p confidence interval is best and AC, score and Jeffreys confidence intervals are next.

• Journal of Korean Society of Transportation
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• v.13 no.1
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• pp.167-183
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• 1995

The objective of study is to evaluate highway capaicty estimation alternative and to develop capacity from statistical distribution of observed traffic flow. Speed-Volume relation is analyzed from vehicle's headway distribution eliminating the long headway by confidence intervals 99%, 95%, 90%. Capacity estimate alternatives were evaluated from 95% , 90%, 85% level of cummulative distribution of observed hourly traffic flow adjusted to confidence intervals. The result of investigation revealed that maximum hourly rate of flow is 2, 130pcu at confidence interval of 995, 2, 233pcu at 95%, 2, 315pcu at 90% respectively. Compared to the capacity of 2, 200pcu per hour per lane used in HCM and KHCM(Korea Highway Capacity Manual), capa챠y appears to correspond to confidence interval of 95%. Using the traffic flow rate at confidence interval of 95% the maximum hourly flow rate is 2, 187pcu at 95% of cummulative volume distribution, 2, 153pcu at 90%, 2, 215pcu at 85%. The study suggests that raional capacity esimation alternative is to take the 95% of cummulative distribution of observed hourly traffic flow at 95% confidence headway interval eliminating 5% long headway.(i.e. 95-95 rule)

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.943-952
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• 2013

Wald, Agresti-Coull, Jeffreys, and Bayes-Laplace methods are commonly used for confidence interval of binomial proportion are applied for prediction intervals. We used coverage probability, mean coverage probability, root mean squared error, and mean expected width for numerical comparisons. From the comparisons, we found that Wald is not proper as for confidence interval and Agresti-Coull is too conservative to differ from confidence interval. However, Jeffrey and Bayes-Laplace are good for prediction interval and Jeffrey is especially desirable as for confidence interval.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.579-588
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• 2021

Wald, Agresti-Coull, Jeffreys, and Bayes-Laplace methods are commonly used for confidence interval of binomial proportion are applied for prediction intervals. We used coverage probability, mean coverage probability, root mean squared error, and mean expected width for numerical comparisons. From the comparisons, we found that Wald is not proper as for confidence interval and Agresti-Coull is too conservative to differ from confidence interval. However, Jeffrey and Bayes-Laplace are good for prediction interval and Jeffrey is especially desirable as for confidence interval.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.895-902
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• 2011

This paper is mainly concerned about providing confidence prediction interval for exchange rate during economic crisis. Our proposed method is to use block bootstrap method for prediction interval for next day. It is shown that block bootstrap method is particularly effective for interval prediction of exchange rate during economic crisis.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.551-562
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• 1995

We consider the iterated bootstrap method in regression model with heterogeneous error variances. The iterated wild bootstrap confidence intervla of the mean response is considered. It is shown that the iterated wild bootstrap confidence interval has coverage error of order $n^{-1}$ wheresa percentile method interval has an error of order $n^{-1/2}$. The simulation results reveal that the iterated bootstrap method calibrates the coverage error of percentile method interval successfully even for the small sample size.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1175-1182
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• 2011

In this paper, properties of exact confidence interval and some approximate confidence intervals of hyper-geometric parameter, that is the probability of success p in the population is discussed. Usually, binomial distribution is a well known discrete distribution with abundant usage. Hypergeometric distribution frequently replaces a binomial distribution when it is desirable to make allowance for the finiteness of the population size. For example, an application of the hypergeometric distribution arises in describing a probability model for the number of children attacked by an infectious disease, when a fixed number of them are exposed to it. Exact confidence interval estimation of hypergeometric parameter is reviewed. We consider the approximation of hypergeometirc distribution to the binomial and normal distribution respectively. Approximate confidence intervals based on these approximation are also adequately discussed. The performance of exact confidence interval estimates and approximate confidence intervals of hypergeometric parameter is compared in terms of actual coverage probability by small sample Monte Carlo simulation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.1
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• pp.67-75
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• 2011

This paper presents a new test technique for evaluating performance of vehicle detectors with interval estimation, not the conventional point estimation, for presenting statistical confidence interval. The methodology is categorized into three parts; sampling plan, analysis on the characteristic of evaluation indices, and the expression of evaluation results. Even though many statistical sampling plans exist, stratified random sampling is regarded as the most appropriate one, considering the detector performance characteristics that varies with traffic, illumination, and meteorological conditions. No magic bullet exists for evaluation index for detector evaluation, hence the characteristics of evaluation indices were thoroughly analyzed and a reasonable process for choosing the best evaluation index is proposed. Finally, the methodology to express the result of detector evaluation for the entire evaluation period and individual analysis interval is represented, respectively. To overcome the existing drawbacks in point estimation, interval estimation by which statistical confidence interval can be represented is introduced for enhancing statistical reliability of traffic detector evaluation. This research can make vehicle detector scheme improve one step forward.