Variable selection for inferential models with relatively high-dimensional data: Between method heterogeneity and covariate stability as adjuncts to robust selection

Variable selection in inferential modelling is problematic when the number of variables is large relative to the number of data points, especially when multicollinearity is present. A variety of techniques have been described to identify ‘important’ subsets of variables from within a large parameter...

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Detalles Bibliográficos
Autores principales: Lima, Eliana, Davies, Peers, Kaler, Jasmeet, Lovatt, Fiona, Green, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224285/
https://www.ncbi.nlm.nih.gov/pubmed/32409668
http://dx.doi.org/10.1038/s41598-020-64829-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224285/
https://www.ncbi.nlm.nih.gov/pubmed/32409668
http://dx.doi.org/10.1038/s41598-020-64829-0

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