• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1387-1395
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• 2006

This paper considers testing for the ratio of two exponential means. We propose a solution based on a Bayesian decision rule to this problem in which no subjective input is considered. The criterion for testing is the Bayesian reference criterion (Bernardo, 1999). We derive the Bayesian reference criterion for testing the ratio of two exponential means. Simulation study and a real data example are provided.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.205-213
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• 2006

This paper considers testing for the ratio of two exponential means. We propose a solution based on a Bayesian decision rule to this problem in which no subjective input is considered. The criterion for testing is the Bayesian reference criterion (Bernardo, 1999). We derive the Bayesian reference criterion for testing the ratio of two exponential means. Simulation study and a real data example are provided.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.176-184
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• 1995

In an exponential distribution, the properties of the interior and exterior trimmed means will be introduced, and reliability estimators using the two trimmed means will be compared with the UMVUE of reliability function through simulations.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.671-672
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• 2008

K-means is a popular one in clustering algorithms, and it minimizes the mutual euclidean distance among the sample points. But K-means has some demerits, such as depending on initial condition, unsupervised learning and local optimum. However mahalanobis distancecan deal this case well. In this paper, the author proposed a new clustering algorithm, named exponential probability clustering, which applied Mahalanobis distance into K-means clustering. This new clustering does possess not only the probability interpretation, but also clustering merits. Finally, the simulation results also demonstrate its good performance compared to K-means algorithm.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.677-687
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• 2004

We consider the comparison of two one-parameter exponential distributions with the complete data as well as the type II censored data. We adapt Bayesian test procedure for nested hypothesis based on the Bayesian reference criterion. Specifically we derive the expression for the Bayesian reference criterion to solve our problem. Also we provide numerical examples using simulated data sets to illustrate our results.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.273-284
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• 2004

Bayes estimation of parameters is considered for two independent exponential distributions with ordered means. Order restricted Bayes estimators for means are obtained with respect to inverted gamma, noninformative prior and uniform prior distributions, and their asymptotic properties are established. It is shown that the maximum likelihood estimator, restricted maximum likelihood estimator, unrestricted Bayes estimator, and restricted Bayes estimator of the mean are all consistent and have the same limiting distribution. These estimators are compared with the corresponding unrestricted Bayes estimators by Monte Carlo simulation.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.135-144
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• 2005

In this paper, we develop the noninformative priors for the exponential models when the parameter of interest is the difference of two means. We develop the first and second order matching priors. We reveal that the second order matching priors do not exist. It turns out that Jeffreys' prior does not satisfy a first order matching criterion. The Bayesian credible intervals based on the first order matching meet the frequentist target coverage probabilities much better than the frequentist intervals of Jeffreys' prior.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.763-772
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• 2015

In this paper, we develop the noninformative priors for the product of different powers of k means in the exponential distribution. We developed the first and second order matching priors. It turns out that the second order matching prior matches the alternative coverage probabilities, and is the highest posterior density matching prior. Also we revealed that the derived reference prior is the second order matching prior, and Jeffreys' prior and reference prior are the same. We showed that the proposed reference prior matches very well the target coverage probabilities in a frequentist sense through simulation study, and an example based on real data is given.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1067-1078
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• 2005

In this paper, we develop the noninformative priors for the exponential models when the parameter of interest is the difference of two means. We develop the first and second order matching priors. We reveal that the second order matching priors do not exist. It turns out that Jeffreys' prior does not satisfy the first order matching criterion. The Bayesian credible intervals based on the first order matching meet the frequentist target coverage probabilities much better than the frequentist intervals of Jeffreys' prior.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.75-80
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• 1994

Parametric estimators of two parameter exponential distribution in the presence of unidentified outliers are proposed, and the means and variances of the estimators are obtained as the exact forms.