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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...
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| Publicado no: | PLoS One |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Public Library of Science
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6980495/ https://ncbi.nlm.nih.gov/pubmed/31978151 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0228154 |
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