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Nonparametric regression with adaptive truncation via a convex hierarchical penalty

We consider the problem of nonparametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well suited to high-dimensional sparse additive models and combines the appealing features of finite basis representation and sm...

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Detalles Bibliográficos
Publicado en:Biometrika
Main Authors: Haris, Asad, Shojaie, Ali, Simon, Noah
Formato: Artigo
Idioma:Inglês
Publicado: Oxford University Press 2019
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Acceso en liña:https://ncbi.nlm.nih.gov/pmc/articles/PMC6691776/
https://ncbi.nlm.nih.gov/pubmed/31427821
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/biomet/asy056
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