Large numbers of explanatory variables: a probabilistic assessment

Recently, Cox and Battey (2017 Proc. Natl Acad. Sci. USA 114, 8592–8595 (doi:10.1073/pnas.1703764114)) outlined a procedure for regression analysis when there are a small number of study individuals and a large number of potential explanatory variables, but relatively few of the latter have a real e...

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Detalles Bibliográficos
Autores principales: Battey, H. S., Cox, D. R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2018
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6083238/
https://www.ncbi.nlm.nih.gov/pubmed/30108456
http://dx.doi.org/10.1098/rspa.2017.0631
Descripción
Sumario:Recently, Cox and Battey (2017 Proc. Natl Acad. Sci. USA 114, 8592–8595 (doi:10.1073/pnas.1703764114)) outlined a procedure for regression analysis when there are a small number of study individuals and a large number of potential explanatory variables, but relatively few of the latter have a real effect. The present paper reports more formal statistical properties. The results are intended primarily to guide the choice of key tuning parameters.

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