• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.13.2-13
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• 2003

We propose some properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses versus on with loss function.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.199-202
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• 2006

We propose some properties of fuzzy p-value and fuzzy significance level to the test statistics for the fuzzy hypotheses testing. Appling the principle of agreement index, we suggest two method for fuzzy hypothesis testing by fuzzy rejection region and fuzzy p-value with fuzzy hypothesis $H_{f,0}$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.45-48
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• 2003

We propose some properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses H$\sub$0/($\theta$) versus H$_1$($\theta$) on $\theta$ with loss function.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.46-49
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• 2002

We propose some properties of hybrid number the addition of fuzzy number and random variable and test the fuzzy hypotheses by agreement index for the hybrid number.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.349-352
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• 2001

We propose one properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses H$\sub$0/($\theta$) versus H$_1$($\theta$) on $\theta$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.255-258
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• 2000

We prepose fuzzy statistical test of fuzzy hypotheses membership function with fuzzy number data. Finding the maximum grade of the meeting point for fuzzy hypotheses membership function and membership function of confidence interval. By the maximum grade, we obtain the results to acceptance or reject for the test of fuzzy hypotheses.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.153-156
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• 2006

We propose some properties of fuzzy p-value and fuzzy significance level to the test statistics for the fuzzy hypotheses testing. Appling the principle of agreement index, we suggest two method for fuzzy hypothesis testing by fuzzy rejection region and fuzzy p-value with fuzzy hypothesis to separately ${\alpha}-level$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.225-229
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• 2007

We propose some properties of fuzzy p-value and fuzzy significance level to the fuzzy hypotheses testing for vague response data with reflected correlation in survey research, Appling the principle of agreement index, we suggest the methods for fuzzy hypothesis testing by fuzzy rejection region.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.614-618
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• 2007

We propose some properties of fuzzy p-value and fuzzy significance level to the fuzzy hypotheses testing for vague response data with reflected correlation in survey research, Appling the principle of agreement index, we suggest the methods for fuzzy hypothesis testing by fuzzy rejection region.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.205-208
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• 2005

We have fuzzy hypotheses testing from Bayesian statistics with ideas from fuzzy sets theory to generalize Bayesian methods both for samples of fuzzy data and for prior distributions with non-precise parameters. Appling the principle of agreement index, the posterior odds ratio in the favor of hypotheses $H_0$ is equal to product of the fuzzy odds ratio and the fuzzy likelihood ratio. If the Posterior odds ratio exceeds the grade judgement, we accept the hypothesis $H_0$ for the degree.