Minimax Rates of <i>ℓ</i><sub><i>p</i></sub>-Losses for High-Dimensional Linear Errors-in-Variables Models over <i>ℓ</i><sub><i>q</i></sub>-Balls

Minimax Rates of <i>ℓ</i><sub><i>p</i></sub>-Losses for High-Dimensional Linear Errors-in-Variables Models over <i>ℓ</i><sub><i>q</i></sub>-Balls

In this paper, the high-dimensional linear regression model is considered, where the covariates are measured with additive noise. Different from most of the other methods, which are based on the assumption that the true covariates are fully obtained, results in this paper only require that the corru...

Full description

Bibliographic Details
Main Authors: Xin Li, Dongya Wu
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Entropy
Subjects:
sparse linear regression
errors-in-variables model
minimax rate
Kullback–Leibler divergence
information-theoretic limitations
Online Access:https://www.mdpi.com/1099-4300/23/6/722

Internet

https://www.mdpi.com/1099-4300/23/6/722

Similar Items