Soft Computing

, Volume 13, Issue 6, pp 617–625

Testing fuzzy hypotheses based on fuzzy test statistic

Original Paper

DOI: 10.1007/s00500-008-0339-3

Cite this article as:
Taheri, S.M. & Arefi, M. Soft Comput (2009) 13: 617. doi:10.1007/s00500-008-0339-3


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 levelFuzzy hypothesisFuzzy test statisticTesting hypothesis

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