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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Detalles Bibliográficos
Autores principales: Deng, Hao, Chen, Jianghong, Song, Biqin, Pan, Zhibin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224641/
https://www.ncbi.nlm.nih.gov/pubmed/34067420
http://dx.doi.org/10.3390/e23060651

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224641/
https://www.ncbi.nlm.nih.gov/pubmed/34067420
http://dx.doi.org/10.3390/e23060651

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