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Weighted LAD-Liu-LASSO for robust estimation and sparsity

Author

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  • Murat Genç

    (Tarsus University)

  • Adewale Lukman

    (University of North Dakota)

Abstract

The Least Absolute Shrinkage and Selection Operator (LASSO) is widely used for parameter estimation and variable selection but can encounter challenges with outliers and heavy-tailed error distributions. Integrating variable selection methods such as LASSO with Weighted Least Absolute Deviation (WLAD) has been explored in limited studies to handle these problems. In this study, we proposed the integration of Weighted Least Absolute Deviation with Liu-LASSO to handle variable selection, parameter estimation, and heavy-tailed error distributions due to the advantages of the Liu-LASSO approach over traditional LASSO methods. This approach is demonstrated through a simple simulation study and real-world application. Our findings showcase the superiority of our method over existing techniques while maintaining the asymptotic efficiency comparable to the unpenalized LAD estimator.

Suggested Citation

  • Murat Genç & Adewale Lukman, 2025. "Weighted LAD-Liu-LASSO for robust estimation and sparsity," Computational Statistics, Springer, vol. 40(9), pages 5075-5104, December.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:9:d:10.1007_s00180-025-01605-6
    DOI: 10.1007/s00180-025-01605-6
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