References:
1. Viertl, R., Statistical Methods for Fuzzy Data, Chichester: Wiley (2011).
2. Corral, N. and Gil, M.A. "A note on interval estimation with fuzzy data", Fuzzy Sets and Systems, 28, pp. 209-215 (1988).
3. Parchami, A., Mashinchi, M., and Maleki, H.R. "Fuzzy confidence intervals for fuzzy process capability index", Journal of Intelligent and Fuzzy Systems, 17, pp. 287- 295 (2006).
4. Ramezani, Z., Parchami, A., and Mashinchi, M. "Fuzzy confidence regions for the Taguchi capability index", International Journal of Systems Science, 42, pp. 977-987 (2011).
5. Wu, H.C. "Statistical confidence intervals for fuzzy data", Expert Systems with Applications, 36, pp. 2670- 2676 (2009).
6. Skrjanc, I. "Confidence interval of fuzzy models: an example using a waste-water treatment plant", Chemometrics and Intelligent Laboratory Systems, 96, pp. 182-187 (2009).
7. Couso, I. and Sanchez, L. "Inner and outer fuzzy approximations of confidence intervals", Fuzzy Sets and Systems, 184, pp. 68-83 (2011).
8. Chachi, J. and Taheri, S.M. "Fuzzy confidence intervals for mean of Gaussian fuzzy random variables", Expert Systems with Applications, 38, pp. 5240-5244 (2011).
9. Kahraman, C., Otay, I., and Oztaysi, B. "Fuzzy extensions of confidence intervals: Estimation for, 2, and p", In Fuzzy Statistical Decision-Making, Studies in Fuzziness and Soft Computing, Kahraman C., Kabak O., Eds., 343, Springer, Cham, pp. 129-154 (2016).
10. Kahraman, C., Oztaysi, B., and Cevik Onar, S. "Interval-valued intuitionistic fuzzy confidence intervals", Journal of Intelligent Systems, 28, pp. 1-13 (2019).
11. Berkachy, R. and Donze, L. "Fuzzy confidence interval estimation by likelihood ratio", 11th Conference of the European Society for Fuzzy Logic and Technology, Atlantis Studies in Uncertainty Modelling, 1, pp. 150- 157 (2019).
12. Chukhrova, N. and Johannssen, A. "Nonparametric fuzzy hypothesis testing for quantiles applied to clinical characteristics of COVID19", International Journal of Intelligent Systems, 36, pp. 2922-2963 (2021).
13. Hesamian, G. and Akbari, M.G. "Testing hypotheses for multivariate normal distribution with fuzzy random variables", International Journal of Systems Science, Published Online, 53(1), pp. 14-24 (2022).
14. Chachi, J., Taheri, S.M., and Viertl, R. "Testing statistical hypotheses based on fuzzy confidence intervals", Austrian Journal of Statistics, 41, pp. 267-286 (2012).
15. Harikrishnan, M., Sundarrajan, J., and Rengasamy, M. "An introduction to fuzzy testing of multialternative hypotheses for group of samples with the single parameter: Through the fuzzy confidence interval of region of acceptance", Journal of Applied Mathematics, 2015, Article no. 365304 (2015).
16. Chukhrova, N. and Johannssen, A. "Fuzzy hypothesis testing: systematic review and bibliography", Applied Soft Computing, 106, pp. 107-331 (2021).
17. Akbari, M.G. and Hesamian, G. "Neyman-pearson lemma based on intuitionistic fuzzy parameters",Soft Computing, 23(14), pp. 5905-5911 (2019).
18. Arefi, M. "Testing statistical hypotheses under fuzzy data and based on a new signed distance", Iranian Journal of Fuzzy Systems, 15(3), pp. 153-176 (2018).
19. Chachi, J. and Taheri, S.M. "Optimal statistical tests based on fuzzy random variables", Iranian Journal of Fuzzy Systems, 15(5), pp. 27-45 (2018).
20. Haktanir, E. and Kahraman, C. "Z-Fuzzy hypothesis testing in statistical decision making", Journal of Intelligent Systems Fuzzy Systems, 37(5), pp. 6545- 6555 (2019).
21. Hryniewicz, O. "Statistical properties of the fuzzy pvalue", International Journal of Approximate Reasoning, 93, pp. 544-560 (2018).
22. Lubiano, M.A., Salas, A., and Gil, M.A. "A hypothesis testing-based discussion on the sensitivity of means of fuzzy data with respect to data shape", Fuzzy Sets and Systems, 328(3), pp. 54-69 (2017).
23. Parchami, A., Taheri, S.M., Viertl, R., et al. "Minimax test for fuzzy hypotheses", Statistical Papers, 59(4), pp. 1623-1648 (2018).
24. Buckley, J.J. "Fuzzy statistics: hypothesis testing", Soft Computing, 9, pp. 512-518 (2005).
25. Buckley, J.J. "Fuzzy statistics: regression and prediction", Soft Computing, 9, pp. 769-775 (2005).
26. Falsafain, A., Taheri, S.M., and Mashinchi, M. "Fuzzy estimation of parameters in statistical models", International Journal of Mathematics Sciences, 2, pp. 79- 85 (2008).
27. Falsafain, A. and Taheri S.M. "On Buckley's approach to fuzzy estimation", Soft Computing, 15, pp. 345-349 (2010).
28. Mylonas, N. and Papadopoulos, B. "Unbiased fuzzy estimators in fuzzy hypotheses testing", Algorithms, 14, pp. 1-23 (2021).
29. Mylonas, N. and Papadopoulos, B. "Fuzzy p-value of hypotheses tests with crisp data using non-asymptotic fuzzy estimators", Journal of Stochastic Analysis, 2(1), pp. 1-35 (2021).
30. Klir, G.J. and Yuan, B., Fuzzy Sets and Fuzzy Logic -Theory and Application, Upper Saddle River: Prentice Hall Inc. (1995).
31. Parchami, A. and Mashinchi, M. "A new generation of process capability indices", Journal of Applied Statistics, 37, pp. 77-89 (2010).
32. Dubois, D. and Prade, H., Possibility Theory: An Approach to Computerized Processing of Uncertainty, New York: Plenum Press (1988).
33. R Core Team, R: A language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, URL: https://www.Rproject. org/ (2018).
34. Meini, S., Suardi, L.R., Busoni, M., et al. "Olfactory and gustatory dysfunctions in 100 patients hospitalized for COVID-19: sex differences and recovery time in real-life", European Archives of Oto-Rhino- Laryngology, Published Online, doi: 10.1007/s00405-020-06102-8 (2020).
35. Bagdonavicius, V., Kruopis, J., and Nikulin, M., Non- Parametric Tests for Complete Data, New Jersey: Wiley (2011).
36. Chukhrova, N. and Johannssen, A. "Fuzzy hypothesis testing for a population proportion based on set-valued information", Fuzzy Sets and Systems, 387, pp. 127- 157 (2020).
37. Parchami, A., Ivani, R., and Mashinchi, M. "An application of testing fuzzy hypotheses: A soil study on bioavailability of Cadmium", Scientia Iranica, 18, pp. 470-478 (2011).