Open AccessParameter estimation of a truncated regression model based on improving numerical optimization algorithms
Author(s) -
Marwan S. Jameel,
Ghalya Tawfeeq Basheer,
Abbas Y. Al-Bayati,
Zakariya Yahya Algamal
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1897/1/012059
Subject(s) - broyden–fletcher–goldfarb–shanno algorithm , algorithm , mean squared error , computer science , monte carlo method , optimization algorithm , regression analysis , estimation theory , variable (mathematics) , mathematical optimization , mathematics , statistics , asynchronous communication , mathematical analysis , computer network
Limited dependent variable models, including truncated regression models, have traditionally been estimated by the method of maximum likelihood. The conventional optimization algorithms; which is known as Quasi-Newton algorithm namely BFGS Quasi-Newton algorithm is used to reach the optimum values for estimated parameters. In this paper, the nature-inspired algorithm is employed to improve the numerical optimization algorithms to better estimation. Our Monte Carlo simulation results suggest that our proposed improving can bring significant improvement relative to others, in terms of mean squared error and prediction mean squared error.