The variable selection methods and algorithms in the multiple linear model

The Lasso and Ridge estimators are effective variable selection methods for a simple linear model. While the multiple linear model is often used in practice, only few studies have examined its problem of variable selection. To address this gap, we study such problem by taking advantage of the Lasso and Ridge estimate. We propose two variable selection methods in the multiple linear model and introduce the multiple random simulation (MRS) algorithm, whose efficiency is similar to that of the least angle regression (LARS) algorithm in a simple linear model. We verify the performance of these two algorithms by applying them to real diabetes data and find that the LARS algorithm cannot be easily applied in a multiple linear model. We also illustrate the excellent performance of MRS by applying this algorithm on a simulated dataset.

Automated summary

This paper addresses the problem of variable selection in multiple linear models by utilizing the Lasso and Ridge estimators. Two variable selection methods are proposed and the efficiency of the multiple random simulation (MRS) algorithm is compared to that of the least angle regression (LARS) algorithm. The performance of these algorithms is verified using real diabetes data and a simulated dataset, demonstrating the excellent performance of MRS.