دمج البوتستراب الاحتمالي الموزون مع طريقة لارس الحصينة لاختيار المُتغيّرات في أنموذج الانحدار الخطّي بوجود مشكلتي الأبعاد العالية والقيم الشاذّة.

Abstract

In this research, a new algorithm was proposed to select the important variables in the regression model with the presence of two problems of high dimensions and outliers by employing and integrating the weighted bootstrap probability - Robust Least Angle Regression Selecting (WBP-LARS) and comparing it with another selection method. It is a method of impregnable Lars based on the regular bootstrap method, known as (B-LARS), empirically simulated and applied, based on real data related to the market value of some private banks in the stock market for the period 2010-2017. The comparison in the simulation included two cases for the required number of explanatory variables Choosing (K = 5, K = 7) as well as two cases when (n>P) (n<P) and sample sizes (50, 70, 20, 26) with a correlation value of 0.95 and with different contamination rates α = (0.05, 0.10, 0.15) The research concluded with conclusions, the most important of which is determining the number of important variables between the numbers 7-10, the preference of the (WBP-LARS) method over the (B-LARS) method when (n>P), while a slight preference for the (B-LARS) method appeared over the proposed method when ( n<P) and the sample size is far from the total number of variables in the model, but the efficiency converges whenever the sample size is very close to the number of variables and can be relied upon in the selection process for the variables in this case