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Testing fuzzy hypotheses based on fuzzy test statistic

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Abstract

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.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    S. Mahmoud Taheri & Mohsen Arefi

  2. Statistical Research and Training Center (SRTC), Tehran, Iran

    S. Mahmoud Taheri

Authors
  1. S. Mahmoud Taheri
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  2. Mohsen Arefi
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Correspondence to S. Mahmoud Taheri.

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Taheri, S.M., Arefi, M. Testing fuzzy hypotheses based on fuzzy test statistic. Soft Comput 13, 617–625 (2009). https://doi.org/10.1007/s00500-008-0339-3

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  • DOI: https://doi.org/10.1007/s00500-008-0339-3

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