Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction
Abstract 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 req...
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| 主要な著者: | , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
BMC
2020-07-01
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| シリーズ: | BMC Medical Research Methodology |
| 主題: | |
| オンライン･アクセス: | http://link.springer.com/article/10.1186/s12874-020-01080-1 |
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