Testing Hypotheses by Fuzzy Methods: A Comparison with the Classical Approach

Author:

Berkachy Rédina,Donzé Laurent

Publisher

Springer International Publishing

Reference9 articles.

1. Berkachy, R., & Donzé, L. (2016). Individual and global assessments with signed distance defuzzification, and characteristics of the output distributions based on an empirical analysis. In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: FCTA (pp. 75–82).

2. Berkachy, R., & Donzé, L. (2017). Testing fuzzy hypotheses with fuzzy data and defuzzification of the fuzzy p-value by the signed distance method. In Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) (pp. 255–264).

3. Berkachy, R., & Donzé, L. (2017). Defuzzification of the fuzzy p-value by the signed distance: Application on real data. In Computational Intelligence. Studies in Computational Intelligence. Berlin: Springer.

4. Berkachy, R., & Donzé, L. (2018). A new approach of testing fuzzy hypotheses by confidence intervals and defuzzification of the fuzzy decision by the signed distance. In METRON. Berlin: Springer.

5. Filzmoser, P., & Viertl, R. (2004). Testing hypotheses with fuzzy data: The fuzzy p-value. In Metrika (Vol. 59, pp. 21–29). Berlin: Springer.

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