2. ﻣﻘﯿﻢ ﺑﯿﮕﯽ، م. (1401)، رﮔﺮﺳﯿﻮن ﻟﻮژﺳﺘﯿﮏ ﭼﻨﺪ ﺟﻤﻠﻪ ای ﻧﯿﻤﻪ ﭘﺎراﻣﺘﺮی ﺑﺮای رده ﺑﻨﺪی داده ﻫﺎی ﺷﮑﻞ، ﻣﺠﻠﻪ ﻋﻠﻮم آﻣﺎری، 16(2)، 449-468.

3. Agresti, A. (2015), Foundations of Linear and Generalized Linear Models, Hoboken: Wiley.

4. Fauci, A., Braunwald, S. E., Kasper, D. L., Hauser, S. L., Longo, D. L., Jameson, J. L., and Loscalzo, J. (2008), Harrison's Principals of Internal Medicine, New York: Wiley. 2275- 2279.

5. Goetschel, R., and Voxman, W. (1986), Elementary Fuzzy Calculus, Fuzzy Sets and Systems, 18, 31-43. [DOI:10.1016/0165-0114(86)90026-6]

6. Gut, A. (2005), Probability, A Graduated Course, Springer Science+Business Media, Inc.

7. Hesamian, G. and Akbari, M. G. (2017), Semi-Parametric Partially Logistic Regression Model with Exact Inputs and Intuitionistic Fuzzy Output Applied, Soft Computing, 58, 517-526. [DOI:10.1016/j.asoc.2017.04.067]

8. Hesamian, G. and Akbari, M. G. (2022), A Fuzzy Empirical Quantile-Based Regression Model Based on Triangular Fuzzy Numbers, Computational and Applied Mathematics, 41(6), 1-26 [DOI:10.1007/s40314-022-01974-4]

9. Joo, S. Y., and Kim, Y. K. (2001), Kolmogorov's Strong Law of Large Numbers for Fuzzy Random Variables, Fuzzy Sets and Systems, 120(3), 499-503. [DOI:10.1016/S0165-0114(99)00140-2]

10. Lee, K. H. (2005), First Course on Fuzzy Theory and Applications, Berlin: Springer.

11. Mirzaei Yeganeh, S., and Taheri, S.M. (2010), Possibilistic Logistic Regression by Linear Programming Approach, Proc of 7th Probability and Random Process, Isfahan University of Technology, Isfahan, Iran, 139-143.

12. Moghimbeygi, M. (2023), Semiparametric Multinomial Logistic Regression Model to Classify‎ ‎Shape Data, Journal of Statistical Sciences, 16(2), 449-468. [DOI:10.52547/jss.16.2.449]

13. Mohd Dom, R. (2009), A Fuzzy Regression Model for The Prediction of Oral Cancer Susceptibility. Ph.D. Thesis, Computer Science and Information Technology, University of Malaya, Kualalumpur.

14. Manski, C., and McFadden, D. (1981), Alternative Estimators and Sample Designs for Discrete Choice Analysis. In: Manski, McFadden, Editors, Structural Analysis of Discrete Data with Econometric Applications, London: MIT Press.

15. Nagar, P., and Srivastava, S. (2008), Adaptive Fuzzy Regression Model for The Prediction of Dichotomous Response Variables Using Cancer Data: A Case Study, Journal of Applied Mathematics Statistics and Informatics, 4, 183-191.

16. Namdari, M., Taheri, S.M., Abadi, A., Rezaei, M., and Kalantari, N. (2013), Possibilistic Logistic Regression for Fuzzy Categorical Response Data, International Conference on Fuzzy Systems, 8, 1-6. [DOI:10.1109/FUZZ-IEEE.2013.6622457] [PMID]

17. Namdari, M., Yoon, J. H., Abadi, A., Taheri, S. M., and Choi, S. H. (2015), Fuzzy Logistic Regression with Least Absolute Deviations Estimators, Soft Computing, 19, 909-917. [DOI:10.1007/s00500-014-1418-2]

18. Pourahmad, S., Ayatollahi, S. M. T., Taheri, S. M., and Agahi, Z. H. (2011.a), Fuzzy Logistic Regression Based on The Least Squares Approach with Application in Clinical Studies, Computer Mathematics Applications, 62, 3353-3365. [DOI:10.1016/j.camwa.2011.08.050]

19. Pourahmad, S., Ayatollahi, S. M., and Taheri, S. M. (2011.b), Fuzzy Logistic Regression, A New Possibilistic Regression and Its Application in Clinical Vague Status, Iranian Journal of Fuzzy Systems, 8, 1-17.

20. Puri, M. L., and Ralescu, D. A. (1986), Fuzzy Random Variables, Journal of Mathematical Analysis and Applications, 114, 409-422. [DOI:10.1016/0022-247X(86)90093-4]

21. Salmani, F., Taheri, S. M., Yoon, J. H., Abadi, A., Alavi Majd, H., and Abbaszadeh, A. (2017), Logistic Regression for Fuzzy Covariates: Modeling, Inference and Applications, International Journal of Fuzzy Systems, 19, 1635-1644. [DOI:10.1007/s40815-016-0258-x]

22. Taheri, S. M., and Mirzaei Yeganeh, S. (2009), Logistic Regression with Non-Precise Response,

23. Proceedings of The 57th ISI Conference, Durban, South Africa, 98-101.

24. Takemura, K. (2004), Fuzzy Logistic Regression Analysis for Fuzzy Input -Output Data, International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems, Japan, 1-6.

25. Tseng, F., and Lin, L. (2005), A Quadratic Interval Logit Model for Forecasting Bankruptcy, Omega, 33, 85-91. [DOI:10.1016/j.omega.2004.04.002]

26. Yu, J. R., and Tseng, F. M. (2014), Fuzzy Piecewise Logistic Growth Model for Innovation Diffusion: A Case Study of The TV Industry, International Journal of Fuzzy Systems, 18, 1-12. [DOI:10.1007/s40815-015-0066-8]

27. Xu, R., and Li, C. (2001), Multidimensional Least-Squares Fitting with A Fuzzy Model, Fuzzy Sets and Systems, 119(2), 215-223. [DOI:10.1016/S0165-0114(98)00350-9]