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Penalized Composite Quasi-Likelihood for Ultrahigh-Dimensional Variable Selection
In high-dimensional model selection problems, penalized least-square approaches have been extensively used. This paper addresses the question of both robustness and efficiency of penalized model selection methods, and proposes a data-driven weighted linear combination of convex loss functions, toget...
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| Autors principals: | , , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2011
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3094588/ https://ncbi.nlm.nih.gov/pubmed/21589849 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1111/j.1467-9868.2010.00764.x |
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