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A Comparative study of traditional model selection methods with some of the regularization methods | ||
| AL-Qadisiyah Journal For Administrative and Economic sciences | ||
| Volume 28, Issue 1, April 2026, Pages 45-56 PDF (469.58 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33916/qjae.2026.014556 | ||
| Authors | ||
| Mohammed H. AL-Sharoot; Amal H. Alwan | ||
| University of AL-Qadisiya | ||
| Abstract | ||
| Linear regression models are used to describe and estimate the relationship between a response variable and a set of covariates. However, if some of covariates are inactive in the regression, the estimated relationship could be unstable and unpredictable. Many methods have been used over the years to identify the active covariates in the regression. In this paper, we propose Bayesian bridge-randomized expectile regression (BBRER). We compare the proposed method with the traditional model selection methods with Lasso and adaptive Lasso methods. Simulation methods show that all methods are perform comparably, however; Lasso performs the best in 80% of the simulation studies. Real data analyses using prostate cancer data also show that Lasso is the best. | ||
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