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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2020
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| 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 |