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...

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Detalles Bibliográficos
Autores principales: Kammer, Michael, Dunkler, Daniela, Michiels, Stefan, Heinze, Georg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
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

Internet

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

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