Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction

BACKGROUND: Missing data are common in statistical analyses, and imputation methods based on random forests (RF) are becoming popular for handling missing data especially in biomedical research. Unlike standard imputation approaches, RF-based imputation methods do not assume normality or require spe...

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Detalles Bibliográficos
Autores principales: Hong, Shangzhi, Lynn, Henry S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382855/
https://www.ncbi.nlm.nih.gov/pubmed/32711455
http://dx.doi.org/10.1186/s12874-020-01080-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382855/
https://www.ncbi.nlm.nih.gov/pubmed/32711455
http://dx.doi.org/10.1186/s12874-020-01080-1

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