Adaptive CoCoLasso for High-Dimensional Measurement Error Models
A significant portion of theoretical and empirical studies in high-dimensional regression have primarily concentrated on clean datasets. However, in numerous practical scenarios, data are often corrupted by missing values and measurement errors, which cannot be ignored. Despite the substantial progr...
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| Formato: | Artículo |
| Lenguaje: | English |
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MDPI AG
2025-01-01
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| Colección: | Entropy |
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| Acceso en línea: | https://www.mdpi.com/1099-4300/27/2/97 |