Comparison Different Estimation Methods for the Parameters of Non-Linear Regression

Zaid  AdilabdAlkareem; Bayda Atiya Kalaf

doi:10.24996/ijs.2022.63.4.24

Authors

Zaid AdilabdAlkareem Department of Mathematics, College of Education for Pure Sciences, Ibn Al –Haitham ,University of Baghdad
Bayda Atiya Kalaf University of Baghdad http://orcid.org/0000-0003-1136-0055

DOI:

https://doi.org/10.24996/ijs.2022.63.4.24

Keywords:

Nonlinear Regression Analysis, Bat algorithm, Simulation, Maximum likelihood method, least square method

Abstract

Nonlinear regression models are important tools for solving optimization problems. As traditional techniques would fail to reach satisfactory solutions for the parameter estimation problem. Hence, in this paper, the BAT algorithm to estimate the parameters of Nonlinear Regression models is used . The simulation study is considered to investigate the performance of the proposed algorithm with the maximum likelihood (MLE) and Least square (LS) methods. The results show that the Bat algorithm provides accurate estimation and it is satisfactory for the parameter estimation of the nonlinear regression models than MLE and LS methods depend on Mean Square error.