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Title (Arabic)

تحسين مقدرات المتغيرات المساعدة بطريقة جاكنايف " باستعمال صنف من أصناف خوارزمية المناعة مع تطبيق عملي

DOI

10.33095/jeas.v25i113.1707

Abstract

Most robust methods are based on the principle of making a strategic trade-off, sacrificing certain aspects to strengthen others through various techniques. In contrast, artificial intelligence mechanisms attempt to balance strengths and weaknesses to reach optimal solutions via stochastic search methods. This research introduces a novel approach to improving the parameter estimators of linear simultaneous equation models derived from the Jackknife Instrumental Variable Estimation (JIVE) method. This is achieved by employing a specific class of the Immune Algorithm known as the Clonal Selection Algorithm (CSA). The results demonstrated improved estimators when evaluated using a robust performance criterion, specifically the Mean Absolute Percentage Error (MAPE). The findings confirm the effectiveness of the utilized AI mechanisms in enhancing the estimators of the linear simultaneous equation model, as evidenced by the adopted criterion and real-world data with a sample size of 48.

Abstract (Arabic)

Most of the robust methods based on the idea of ​​sacrificing one side versus promotion of another, the artificial intelligence mechanisms try to balance weakness and strength to make the best solutions in a random search technique. In this paper, a new idea is introduced to improve the estimators of parameters of linear simultaneous equation models that resulting from the Jackknife Instrumental Variable Estimation method (JIVE) by using a class of immune algorithm which called Clonal Selection Algorithm (CSA) and better estimates are obtained using one of the robust criterion which is called Mean Absolut Percentage Error (MAPE). The success of intelligence algorithm mechanisms has been proven that used to improve the parameters of linear simultaneous equation models according to user criterion and real data of size n=48.

First Page

462

Last Page

474

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