Soft Computing

, Volume 13, Issue 6, pp 617–625 | Cite as

Testing fuzzy hypotheses based on fuzzy test statistic

Original Paper


A new approach for testing fuzzy parametric hypotheses based on fuzzy test statistic is introduced. First, we define some models representing the extended versions of the simple, the one-sided and the two-sided crisp hypotheses to the fuzzy ones. Then, we provide a confidence interval for interested parameter, and using α-cuts of the fuzzy null hypothesis, we construct the related fuzzy test statistic. Finally, by introducing a credit level, we can decide to accept or reject the fuzzy hypothesis. The method is applied to test the fuzzy hypotheses for the mean of a normal distribution, the variance of a normal distribution, and the mean of a Poisson distribution.


Credit level Fuzzy hypothesis Fuzzy test statistic Testing hypothesis 


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of Mathematical SciencesIsfahan University of TechnologyIsfahanIran
  2. 2.Statistical Research and Training Center (SRTC)TehranIran

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