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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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| Gepubliceerd in: | Biometrika |
|---|---|
| Hoofdauteurs: | , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
Oxford University Press
2019
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| Onderwerpen: | |
| Online toegang: | 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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