A New Quantile-Based Approach for LASSO Estimation

Ismail Shah, Hina Naz, Sajid Ali (), Amani Almohaimeed () and Showkat Ahmad Lone
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Ismail Shah: Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Hina Naz: Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Sajid Ali: Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Amani Almohaimeed: Department of Statistics and Operation Research, College of Science, Qassim University, Buraydah 51482, Saudi Arabia
Showkat Ahmad Lone: Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi Arabia

Mathematics, 2023, vol. 11, issue 6, 1-13

Abstract: Regularization regression techniques are widely used to overcome a model’s parameter estimation problem in the presence of multicollinearity. Several biased techniques are available in the literature, including ridge, Least Angle Shrinkage Selection Operator (LASSO), and elastic net. In this work, we study the performance of the classical LASSO, adaptive LASSO, and ordinary least squares (OLS) methods in high-multicollinearity scenarios and propose some new estimators for estimating the LASSO parameter “k”. The performance of the proposed estimators is evaluated using extensive Monte Carlo simulations and real-life examples. Based on the mean square error criterion, the results suggest that the proposed estimators outperformed the existing estimators.

Keywords: LASSO; regularization methods; multicollinearity; high-dimensional data; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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