• Title/Summary/Keyword: Testing Hypotheses

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A Bayesian Fuzzy Hypotheses Testing with Loss Function (손실함수에 의한 베이지안 퍼지 가설검정)

  • 강만기;한성일;최규탁
    • 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.

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Bayesian Hypotheses Testing for the Weibull Lifetime Data (와이블 수명자료들에 대한 베이지안 가설검정)

  • 강상길;김달호;조장식
    • Journal of Korean Society for Quality Management
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    • v.28 no.3
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    • pp.1-10
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    • 2000
  • In this paper, we address the Bayesian hypotheses testing for the comparison of Weibull distributions. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are Improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic and median intrinsic Bayes factors for the comparison of Weibull lifetime model and we use these results to analyze real data sets.

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Hypotheses Testing for the Shape Parameter of the Weibull Lifetime Data

  • Kang, Sang-Gil;Kim, Dal-Ho;Cho, Jang-Sik
    • Journal of Korean Society for Quality Management
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    • v.27 no.4
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    • pp.153-166
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    • 1999
  • In this paper, we address the Bayesian hypotheses testing for the shape parameter of weibull model. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic and median intrinsic Bayes factors and use these results to analyze real data sets.

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Fuzzy hypotheses testing by fuzzy p-value (퍼지 p-값에 의한 퍼지가설검정)

  • Kang Man-Ki;Choi Gue-Tak
    • 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 $H_{f,0}$.

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Bayesian an Fuzzy Hypotheses by Revision of Possibility Distribution (실현성분포의 개정에 의한 베이지안 퍼지 가설 검정)

  • Kang, Man-Ki;Lee, Chang-Eun;Park, Kue-Tak
    • 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 $\theta$.

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Comparison of methods for the proportion of true null hypotheses in microarray studies

  • Kang, Joonsung
    • Communications for Statistical Applications and Methods
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    • v.27 no.1
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    • pp.141-148
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    • 2020
  • We consider estimating the proportion of true null hypotheses in multiple testing problems. A traditional multiple testing rate, family-wise error rate is too conservative and old to control type I error in multiple testing setups; however, false discovery rate (FDR) has received significant attention in many research areas such as GWAS data, FMRI data, and signal processing. Identify differentially expressed genes in microarray studies involves estimating the proportion of true null hypotheses in FDR procedures. However, we need to account for unknown dependence structures among genes in microarray data in order to estimate the proportion of true null hypothesis since the genuine dependence structure of microarray data is unknown. We compare various procedures in simulation data and real microarray data. We consider a hidden Markov model for simulated data with dependency. Cai procedure (2007) and a sliding linear model procedure (2011) have a relatively smaller bias and standard errors, being more proper for estimating the proportion of true null hypotheses in simulated data under various setups. Real data analysis shows that 5 estimation procedures among 9 procedures have almost similar values of the estimated proportion of true null hypotheses in microarray data.

Bayesian Testing for Independence in Bivariate Exponential Model

  • Cho, Jang-Sik
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.2
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    • pp.521-527
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    • 2006
  • In this paper, we consider the Bayesian hypotheses testing for independence in bivariate exponential model. In Bayesian testing problem, we use the noninformative priors for parameters which are improper and are defined only up to arbitrary constants. And we use the recently proposed hypotheses testing criterion called the fractional Bayes factor. Also we give some numerical results to illustrate our results.

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Hypotheses testing of Bayes' theorem for fuzzy prior parameters (퍼지 사전 모수에 관한 베이지안 가설검정)

  • Kang Man-Ki;Chio Gue-Tak
    • 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.

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