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

Al Kut Journal of Economics and Administrative Sciences Pub Date : 2023-06-17 DOI:10.29124/kjeas.1547.19

Zenah Hikmet, Basim Shlaiba

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

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加权概率自举法与鲁棒Lars方法的集成用于高维异常值线性回归模型的变量选择

针对存在高维和离群值两大问题的回归模型，提出了一种新的选择算法——鲁棒最小角度回归选择(Robust Least Angle regression selection, WBP-LARS)，并将其与另一种选择方法进行比较。它是一种基于常规bootstrap方法的坚不可摧的Lars方法，称为(B-LARS)，基于2010-2017年部分私人银行股票市场市值相关的真实数据进行实证模拟和应用。仿真中的比较包括(K = 5, K = 7)所需解释变量数选择的两种情况以及(n>P) (nP)的两种情况，而当(n

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Al Kut Journal of Economics and Administrative Sciences

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