3. Basellini, U., Camarda C. G. and Booth, H. (2023). Thirty Years on: A Review of the Lee-Carter Method for Forecasting Mortality. International Journal of Forecasting, 39, 1033-1049. [DOI:10.1016/j.ijforecast.2022.11.002]

4. Chachi, J. (2019). A Weighted Least Squares Fuzzy Regression for Crisp Input-Fuzzy Output Data. IEEE Transactions on Fuzzy Systems, 27(4), 739-748. [DOI:10.1109/TFUZZ.2018.2868554]

5. Chachi J. and Chaji A. (2021). Employing Weighted Operators in Ordered Least Deviations Regression Model. Journal of Statistical Sciences, 15(1), 39-60. [DOI:10.52547/jss.15.1.3]

6. Chachi, J., Kazemifard, A. and Jalalvand, M. (2021). A Multi-Attribute Assessment of Fuzzy Regression Models. Iranian Journal of Fuzzy Systems, 18(4), 131-148.

7. Chachi, J., Taheri, S. M. and D'Urso, P. (2022). Fuzzy Regression Analysis Based on M-Estimates. Expert Systems with Applications, 187, 115891. [DOI:10.1016/j.eswa.2021.115891]

8. D'Urso, P. and Chachi, J. (2022). OWA Fuzzy Regression. International Journal of Approximate Reasoning, 142, 430-450. [DOI:10.1016/j.ijar.2021.12.009]

9. Ferraro, M. B. (2017). On the Generalization Performance of a Regression Model with Imprecise Elements. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 25(5), 723-740. [DOI:10.1142/S0218488517500313]

10. Ignataviciute, E., Mikalauskaite-Arminiene, R. and Siaulys, J. (2012). Lee-Carter Mortality Forecasting, Lithuanian Journal of Statistics, 51(1), 22-35. [DOI:10.15388/LJS.2012.13903]

11. James, G., Witten, D., Hastie, T., Tibshirani, R. and Taylor, J. (2023). An Introduction to Statistical Learning: with Applications in Python. Cham: Springer International Publishing. [DOI:10.1007/978-3-031-38747-0]

12. Kazemifard, A. and Chachi, J. (2022). MADM Approach to Analyse the Performance of Fuzzy Regression Models. Journal of Ambient Intelligence and Humanized Computing, 13(8), 4019-4031. [DOI:10.1007/s12652-021-03394-4]

13. Koissi, M. C. and Shapiro, A. F. (2006). Fuzzy Formulation of the Lee-Carter Model for Mortality Forecasting. Insurance: Mathematics and Economics, 39(3), 287-309. [DOI:10.1016/j.insmatheco.2005.11.011]

14. Krätschmer, V. (2001). A Unified Approach to Fuzzy Random Variables. Fuzzy sets and systems, 123, 1-9. [DOI:10.1016/S0165-0114(00)00038-5]

15. Kruse, R. and Meyer, K. D. (1987). Statistics with Vague Data. Springer Science and Business Media. [DOI:10.1007/978-94-009-3943-1]

16. Lee, R. D. (2000). The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications. North American Actuarial Journal, 1(4), 80-91. [DOI:10.1080/10920277.2000.10595882]

17. Lee, R.D. and Carter, L.R. (1992). Modeling and Forecasting U.S. Mortality. J. Amer. Statist. Assoc., 419(87), 659-675. https://doi.org/10.2307/2290201 [DOI:10.1080/01621459.1992.10475265]

18. Mohammadi, H., Akbari, M. G. and Hesamian, G. (2024). Autoregressive Modeling Based on the Support Function of Fuzzy Random Variables. Journal of Statistical Sciences, 18(1), 173-192.

19. Shmueli, G. and Polak, J. (2024). Practical Time Series Forecasting with R: A Hands-on Guide. Axelrodschnall publishers.

20. Szymanski, A. and Rossa, A. (2021). The Modified Fuzzy Mortality Model Based on the Algebra of Ordered Fuzzy Numbers. Biometrical Journal, 63(3), 671-689. [DOI:10.1002/bimj.202000025] [PMID]

21. Tsukada, M., Kobayashi, Y., Kaneko, H., Takahasi, S.E., Shirayanagi, K. and Noguchi, M. (2023). Linear Algebra with Python. Springer Undergraduate Texts in Mathematics and Technology. Springer, Singapore. [DOI:10.1007/978-981-99-2951-1]

22. Zadeh, L.A. (1965). Fuzzy Sets. Information and Control, 8, 338-353. [DOI:10.1016/S0019-9958(65)90241-X]

23. Zarei, R., Akbari, M.G. and Chachi, J. (2020). Modeling Autoregressive Fuzzy Time Series Data Based on Semi-Parametric Methods. Soft Comput, 24, 7295-7304. [DOI:10.1007/s00500-019-04349-w]

24. Zimmermann, H.J. (2011). Fuzzy Set Theory and its Applications. Springer Science and Business Media.