Integration of the weighted probabilistic bootstrap with the robust Lars method for selecting variables in linear regression model with problems of high dimensions and outliers

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) (nP), while a slight preference for the (B-LARS) method appeared over the proposed method when ( n