Error Bound of Mode-Based Additive Models

Due to their flexibility and interpretability, additive models are powerful tools for high-dimensional mean regression and variable selection. However, the least-squares loss-based mean regression models suffer from sensitivity to non-Gaussian noises, and there is also a need to improve the model’s...

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Bibliographic Details
Main Authors: Hao Deng, Jianghong Chen, Biqin Song, Zhibin Pan
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Entropy
Subjects:
Online Access:https://www.mdpi.com/1099-4300/23/6/651