Fuzzy confidence interval construction and its application in recovery time for COVID-19 patients

Document Type : Article

Authors

1 Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

2 School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

Abstract

An approach is proposed to construct fuzzy confidence intervals for unknown parameters in statistical models. In this approach, a family of confidence intervals of the unknown crisp parameter has been considered. Such confidence intervals are used to obtain a fuzzy confidence interval for the parameter of interest. ‎
The proposed approach benefits a wide range of confidence intervals to obtain a trapezoidal shaped fuzzy set of the parameter space as the fuzzy confidence interval for the parameter of interest. By using the resolution identity, it is shown that the constructed fuzzy confidence intervals are really fuzzy sets of the parameter space.
Some numerical examples are provided to explain the approach in one-sided and two-sided fuzzy confidence intervals. ‎
‎Moreover, ‎an‎‎‎ application in health sciences ‎‎‎is provided about the ‎‎‎‎recovery time of olfactory and gustatory dysfunctions for COVID-19 patients.

Keywords


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).