International Journal of Nonlinear Analysis and Applications

Maximum likelihood for fuzzy pure spatial autoregressive model

Document Type : Research Paper

Authors

1 Department statistics, College Administration and Economics, University of Kirkuk, Kirkuk, Iraq

2 Department Mathematics, College Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq

Abstract

This paper deals with the study about the formulation of spatial regression models for independent and dependent fuzzy variables, while the parameters crisp values, which are estimated by the maximum likelihood method. In this paper, has been formulated Fuzzy Pure Spatial Autoregressive Model (FPSAM)  from the fuzzy general spatial model, and applied for Trapezoidal fuzzy number in the domain traffic accidents for a number of cities in Iraq for the year  2018 and that after converting the Trapezoidal fuzzy number into crisp values by centroid method, calculations the results by Matlab language.

Keywords

[1] Q.N.N. Al-Kazaz and H.J.K. Al-Saadi, Comparing Bayes estimation with maximum likelihood estimation of generalized inverted exponential distribution in case of fuzzy data, J. Econom. Administ. Sci. 23(101) (2017) 1–18.
[2] A.S. Ahmar and M.K. Aidid, Crime modeling using spatial regression approach, J. Phys. Conf. Ser. IOP Publishing, 954 (2018) 012013.
[3] G. Arbia, A Primer for Spatial Econometrics with Applications in R, Springer, 2014.[4] H.M. Elias and H.A. Taha al Dabbagh, Fuzzy regression analysis, Iraqi J. Stat. Sci. 10 (2006) 61–84.
[5] M.M. Fischer and J. Wang, Spatial data analysis: models, methods and techniques, Springer Science and Business Media, 2011.
[6] S.Q. Hadi and O.A.M. Ali, Estimating Spatial Regression Models for Poverty Rates in the Districts of Iraq for the Year 2012, Master’s Thesis in Statistics, College of Administration and Economics, University of Baghdad, 2014.
[7] C. Kao and C.L. Chyu, A fuzzy linear regression model with better explanatory power, Fuzzy Sets Syst. 126 (2002) 401–409.
[8] K. H. Lee, First course on fuzzy theory and applications, Springer Science and Business Media. 2004.
[9] D.B. Omar and A.F. Tawfeeq, Estimating parameters of some spatial regression models with experimental and applied study, Department of Statistics, College of Administration and Economics, University of Kirkuk, 2020.
[10] T.J. Ross, Fuzzy logic with engineering applications, John Wiley and Sons, 2005.
[11] E.S. Tony, Spatial data analysis, University of Pennsylvania School of Engineering and Applied Science, 2015.
[12] Y.J. Wang and H. S. Lee, The revised method of ranking fuzzy numbers with an area between the centroid and original points, Comput. Math. Appl. 55 (2008) 2033–2042.
[13] Y.M. Wang, J.B. Yang, D.L. Xu and K.S. Chin, On the centroids of fuzzy numbers, Fuzzy Sets Syst. 157 (2006) 919–926.
[14] A. Zhang, Statistical analysis of fuzzy linear regression model based on centroid method, Appl. Math. 7 (2016) 579.
International Journal of Nonlinear Analysis and Applications
Volume 13, Issue 1
March 2022
Pages 2267-2277
  • Receive Date: 08 September 2021
  • Revise Date: 14 October 2021
  • Accept Date: 25 November 2021