Regularized least weighted squares estimator in linear regression

0581616 - ÚI 2025 GB eng J - Journal Article
Kalina, Jan
Regularized least weighted squares estimator in linear regression.
Communications in Statistics - Simulation and Computation. Online 08 January 2024 (2024). ISSN 0361-0918. E-ISSN 1532-4141
R&D Projects: GA ČR(CZ) GA22-02067S
Institutional support: RVO:67985807
Keywords : Lasso estimator * Outliers * Regularization * Robust regression * Sparsity
Impact factor: 0.9, year: 2022
Method of publishing: Limited access
https://doi.org/10.1080/03610918.2023.2300356

This article is interested in estimating parameters of the linear regression model in a high-dimensional setting, i.e. with a large number of regressors. The lasso estimator does not possess high robustness with respect to the presence of outliers in the data. Our approach extends the least weighted squares estimator, which has appealing robustness and efficiency properties in linear regression with a small number of regressors. The novel LWS-lasso estimator is proposed here as an L1-regularized version of the least weighted squares. The analysis of a world tourism dataset as well as simulations show that LWS-lasso may outperform available regression estimators, especially in scenarios with high-dimensional data with a higher contamination by outliers.
Permanent Link: https://hdl.handle.net/11104/0349720