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Evaluating methods for Lasso selective inference in biomedical research: a comparative simulation study
BACKGROUND: Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model. This leads to over-optimistic selection and r...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316707/ https://www.ncbi.nlm.nih.gov/pubmed/35883041 http://dx.doi.org/10.1186/s12874-022-01681-y |