Adaptive Penalized Regression for High-Efficiency Estimation in Correlated Predictor Settings: A Data-Driven Shrinkage Approach
Penalized regression estimators have become widely adopted alternatives to ordinary least squares while analyzing collinear data, despite introducing some bias. However, existing penalized methods lack universal superiority across diverse data conditions. To address this limitation, we propose a nov...
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| Principais autores: | , |
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| Format: | Artigo |
| Sprog: | Inglês |
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MDPI AG
2025-09-01
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| Serier: | Mathematics |
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| Online adgang: | https://www.mdpi.com/2227-7390/13/17/2884 |
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