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Penalized logistic regression with low prevalence exposures beyond high dimensional settings
Estimating and selecting risk factors with extremely low prevalences of exposure for a binary outcome is a challenge because classical standard techniques, markedly logistic regression, often fail to provide meaningful results in such settings. While penalized regression methods are widely used in h...
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| Publicado no: | PLoS One |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Public Library of Science
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6527211/ https://ncbi.nlm.nih.gov/pubmed/31107924 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0217057 |
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