Is it time to stop sweeping data cleaning under the carpet? A novel algorithm for outlier management in growth data

All data are prone to error and require data cleaning prior to analysis. An important example is longitudinal growth data, for which there are no universally agreed standard methods for identifying and removing implausible values and many existing methods have limitations that restrict their usage a...

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Detalles Bibliográficos
Autores principales: Woolley, Charlotte S. C., Handel, Ian G., Bronsvoort, B. Mark, Schoenebeck, Jeffrey J., Clements, Dylan N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980495/
https://www.ncbi.nlm.nih.gov/pubmed/31978151
http://dx.doi.org/10.1371/journal.pone.0228154

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980495/
https://www.ncbi.nlm.nih.gov/pubmed/31978151
http://dx.doi.org/10.1371/journal.pone.0228154

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