Testing fuzzy hypotheses based on fuzzy test statistic
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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.
KeywordsCredit level Fuzzy hypothesis Fuzzy test statistic Testing hypothesis
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- Buckley JJ (2005) Fuzzy statistics. Springer, HeidelbergGoogle Scholar
- Delagado M, Verdegay JL, Vila MA (1985) Testing fuzzy-hypotheses, a Bayesian approach. In: Gupta MM et al. (ed) Approximate reasoning in expert systems. North-Holland, Amsterdam, pp 307–316Google Scholar
- Finney RL, Thomas GB (1994) Calculus, 2nd edn. Addison Wesley, New YorkGoogle Scholar