2025, Vol. 6, Issue 2, Part D
Choosing best model selection in elastic-net quantile regression model
Author(s): Shatha Awad Al-Fatlawi and Sanaa J Tuama
Abstract: The effectiveness of model selection techniques is significantly influenced by shrinkage parameters. The more precise the shrinkage parameterization process is, the greater the likelihood of obtaining efficient, generalizable models. One of the methods for selecting important variables is the elastic- net method, which is considered a very efficient method for selecting variables and then selecting models. By utilization the Elastic Net method with the quantile regression model, we will obtain an effective statistical model in selecting models and estimating the coefficients of these models. Two methods will be used in this paper to choose the shrinkage parameter, and the simulation approach and the real data technique were used to determine which of these two methods is better. Based on the results, it was determined that the Bayesian method is the best method for choosing the optimal model after determining the shrinkage parameters in the Elast-net technique.
DOI: https://doi.org/10.22271/math.2025.v6.i2d.256
Pages: 661-666 | Views: 78 | Downloads: 28
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How to cite this article:
Shatha Awad Al-Fatlawi and Sanaa J Tuama. Choosing best model selection in elastic-net quantile regression model. Journal of Mathematical Problems, Equations and Statistics. 2025; 6(2): 661-666. DOI: 10.22271/math.2025.v6.i2d.256
