Weighted LAD-Liu-LASSO for robust estimation and sparsity
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Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Lasso, Least absolute deviation, Weighted estimator, Liu-Lasso, Outliers, Variable selection
Kaynak
Computational Statistics
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
