• 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.11 no.1
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• pp.151-161
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• 1998

We study the bootstrap interval estimation for survival function in the Koziol-Green model. We construct the approximate bootstrap confidence intervals for survival function and prove the strong consistency for the bootstrap estimator of survival function. Finally we show that the approximate bootstrap confidence intervals are better in terms of coverage probability than confidence intervals based on asymptotic normal distribution and transformations of survival function via Monte Carlo simulation study.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.421-433
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• 1998

In this paper, matching priors for P(X < Y) are investigated when both distributions are exponential distributions. Two recent approaches for finding noninformative priors are introduced. The first one is the verger and Bernardo's forward and backward reference priors that maximizes the expected Kullback-Liebler Divergence between posterior and prior density. The second one is the matching prior identified by matching the one sided posterior credible interval with the frequentist's desired confidence level. The general forms of the second- order matching prior are presented so that the one sided posterior credible intervals agree with the frequentist's desired confidence levels up to O(n$^{-1}$ ). The frequentist coverage probabilities of confidence sets based on several noninformative priors are compared for small sample sizes via the Monte-Carlo simulation.

• Speech Sciences
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• v.14 no.4
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• pp.145-156
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• 2007

The performance of speaker identification is severely degraded in noisy environments. Recently probability weighting method based on observation membership was proposed for overcoming the noise problem[1]. In the paper[1] the observation confidence was calculated from SNR with sigmoid function. However, estimating SNR needs additive calculation amount and estimated SNR is corrupted in dynamic noisy environments. In this paper we propose estimation methods of the observation confidence based on score-based reliabilities (SBR) of entropy and dispersion measures. Generally SBRs are obtained from speaker models' probabilities. The proposed methods are evaluated with ETRI speaker recognition DB. We compared the performances of the proposed methods with those in [1][8]. The experimental results show that the proposed methods can be successfully applied for the case where SNR is not available.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.351-361
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• 2003

The previous studies to extract features for document through word association have the problems of updating profiles periodically, dealing with noun phrases, and calculating the probability for indices. We propose more effective feature extraction method which is using association word mining. The association word mining method, by using Apriori algorithm, represents a feature for document as not single words but association-word-vectors. Association words extracted from document by Apriori algorithm depend on confidence, support, and the number of composed words. This paper proposes an effective method to determine confidence, support, and the number of words composing association words. Since the feature extraction method using association word mining does not use the profile, it need not update the profile, and automatically generates noun phrase by using confidence and support at Apriori algorithm without calculating the probability for index. We apply the proposed method to document classification using Naive Bayes classifier, and compare it with methods of information gain and TFㆍIDF. Besides, we compare the method proposed in this paper with document classification methods using index association and word association based on the model of probability, respectively.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.309-317
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• 1997

A sequential procedure for estimating a linear function of two normal means which satisfies the two requirements, i.e. one is a condition of coverage probability, the other is a condition of $\beta$-protection, is proposed when the variances are unknown and not necessarily equal. We give asymptotic behaviors of the proposed stopping time.

• 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 Chungcheong Mathematical Society
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• v.5 no.1
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• pp.87-97
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• 1992

Sequential procedure with ${\beta}$-protection for the mean vector ${\mu}(\theta)$ of a p(> 1)-variate multivariate distribution $P_{\theta}$, ${\theta}{\in}{\Theta}$, with covariance matrix ${\sum}(\theta)$ is considered when the only nuisance parameters is ${\sum}(\theta)$. We obtain a confidence set for ${\mu}(\theta)$ with coverage probability condition and ${\beta}$-protection at ${\mu}-{\delta}(\mu)$ for some imprecision function ${\delta}:\mathbb{R}^p{\rightarrow}\mathbb{R}^p$.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.257-266
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• 2007

It is known that Agresti-Coull approach is an effective tool for the construction of confidence intervals for various problems related to binomial proportions. However, the Agrest-Coull approach often produces a conservative confidence interval. In this note, confidence intervals based on a Bayesian approach are proposed for a linear function of independent binomial proportions. It is shown that the Bayesian confidence interval slightly outperforms the confidence interval based on Agresti-Coull approach in average sense.

• Journal of Korean Society for Quality Management
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• v.22 no.4
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• pp.102-110
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• 1994

We investigate the small-sample behavior of the probability of rejection curves and its performance for a equivalence testing procedure based on confidence intervals which was developed with a motivation from bioequivalence studies. This type of equivalence studies are conducted frequently in pharmaceutical industries to compare the relative bioavailabilty of two formulations of a drug and can be applied various fields where assurance of quality equivalence is needed. From the Monte-Carlo simulation results we suggest proper sample sizes for the equivalence testing procedure.