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Model-free feature screening for categorical outcomes: Nonlinear effect detection and false discovery rate control
Feature screening has become a real prerequisite for the analysis of high-dimensional genomic data, as it is effective in reducing dimensionality and removing redundant features. However, existing methods for feature screening have been mostly relying on the assumptions of linear effects and indepen...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2019
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6544247/ https://www.ncbi.nlm.nih.gov/pubmed/31150453 http://dx.doi.org/10.1371/journal.pone.0217463 |