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Likelihood-based selection and sharp parameter estimation
In high-dimensional data analysis, feature selection becomes one means for dimension reduction, which proceeds with parameter estimation. Concerning accuracy of selection and estimation, we study nonconvex constrained and regularized likelihoods in the presence of nuisance parameters. Theoretically,...
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| Autores principales: | , , |
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| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
2012
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3378256/ https://ncbi.nlm.nih.gov/pubmed/22736876 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/01621459.2011.645783 |
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