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...

Szczegółowa specyfikacja

Opis bibliograficzny
1. autor: Qin Yu
Format: Artykuł
Język:English
Wydane: MDPI AG 2025-01-01
Seria:Entropy
Hasła przedmiotowe:
Dostęp online:https://www.mdpi.com/1099-4300/27/2/97