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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2020
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| Materias: | |
| 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 |