• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1413-1425
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• 2006

When X and Y have independent gamma distributions, we develop a Bayesian procedure for testing the equality of two gamma means. The reference prior is derived. Using the derived reference prior, we propose a Bayesian test procedure for the equality of two gamma means using fractional Bayes factor and intrinsic Bayes factor. Simulation study and a real data example are provided.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2102-2107
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• 2008

This paper demonstrates a enhanced FCM to identify the causes of ground faults in power distribution systems. The discrimination scheme which can automatically recognize the fault causes is proposed using Fuzzy RBF networks. By using the actual fault data, it is shown that the proposed method provides satisfactory results for identifying the fault causes.

• Bulletin of the Korean Mathematical Society
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• v.50 no.2
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• pp.485-498
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• 2013

In this paper, the Schur convexity is generalized to Schur $f$-convexity, which contains the Schur geometrical convexity, harmonic convexity and so on. When $f$ : ${\mathbb{R}}_+{\rightarrow}{\mathbb{R}}$ is defined as $f(x)=(x^m-1)/m$ if $m{\neq}0$ and $f(x)$ = ln $x$ if $m=0$, the necessary and sufficient conditions for $f$-convexity (is called Schur $m$-power convexity) of Gini means are given, which generalize and unify certain known results.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.549-562
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• 2000

Default Bayes factors are computed to test the equality of one Poisson population mean and the equality of two independent Possion population means. As default priors are assumed Jeffreys priors, noninformative improper priors, and default Bayes factors such as three intrinsic Bayes factors of Berger and Pericchi(1996, 1998), the arithmetic, the median, and the geometric intrinsic Bayes factor, and the factional Bayes factor of O'Hagan(1995) are computed. The testing results by each default Bayes factor are compared with those by the classical method in the simulation study.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.6-9
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• 2012

In this study, we compare two image denoising methods which are the Rudin-Osher-Fatemi total variation (TV) model and the nonlocal means (NLM) algorithm on medical images. To evaluate those methods, we used two well known measuring metrics. The methods are tested with a CT image, one X-Ray image, and three MRI images. Experimental result shows that the NML algorithm can give better results than the ROF TV model, but computational complexity is high.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.511-522
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• 1999

The paper considers the problem of robustness in hierarchical bayesian models. In specific we address Bayesian robustness in the estimation of normal means. We provide the ranges of the posterior means under $\varepsilon$-contamination class as well as the density ratio class of priors. For the class of priors that are uniform over a specified interval we investigate the sensitivity as to the choice of the intervals. The methods are illustrated using the famous baseball data of Efron and Morris(1975).

• Kyungpook Mathematical Journal
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• v.45 no.1
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• pp.73-85
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• 2005

In this paper a criterion for $L^1-convergence$ of a new modified sine sums is obtained by using $Ces{\grave{a}}ro$ means of integral and non-integral orders.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.951-958
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• 2010

In this paper, we obtain the restricted maximum likelihood estimators (RMLE's) for means in normal distributions with the ordered mean constraints. The biases and mean squared errors (MSE's) of these RMLE's are approximated by Mote Carlo methods. In every case a substantial savings in MSE is obtained at the expense of a small loss in bias when using RMLE's instead of the unrestricted MLE's.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.1123-1132
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• 2008

In this paper, by introducing a parameter, we establish a new Ostrowski's integral inequality which generalizes the result of [5]. Finally, we apply the new Ostrowski's inequality to numerical integration and special means.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1449-1454
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• 2013

There are many methods for measuring the difference between two location parameters. In this paper, statistics are proposed in order to estimate the difference of two location parameters. The statistics are designed not using the means, variances, signs and ranks, but with the cumulative distribution functions. Hence these are measured as the differences in the area between two univariate cumulative distribution functions. It is found that the difference in the area between two empirical cumulative distribution functions is the difference of two sample means, and its integral is also the difference of two population means.