Missing value imputation with adversarial random forests -- MissARF
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916853999206400 |
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| author | Golchian, Pegah Kapar, Jan Watson, David S. Wright, Marvin N. |
| author_facet | Golchian, Pegah Kapar, Jan Watson, David S. Wright, Marvin N. |
| contents | Handling missing values is a common challenge in biostatistical analyses, typically addressed by imputation methods. We propose a novel, fast, and easy-to-use imputation method called missing value imputation with adversarial random forests (MissARF), based on generative machine learning, that provides both single and multiple imputation. MissARF employs adversarial random forest (ARF) for density estimation and data synthesis. To impute a missing value of an observation, we condition on the non-missing values and sample from the estimated conditional distribution generated by ARF. Our experiments demonstrate that MissARF performs comparably to state-of-the-art single and multiple imputation methods in terms of imputation quality and fast runtime with no additional costs for multiple imputation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15681 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Missing value imputation with adversarial random forests -- MissARF Golchian, Pegah Kapar, Jan Watson, David S. Wright, Marvin N. Machine Learning Artificial Intelligence Handling missing values is a common challenge in biostatistical analyses, typically addressed by imputation methods. We propose a novel, fast, and easy-to-use imputation method called missing value imputation with adversarial random forests (MissARF), based on generative machine learning, that provides both single and multiple imputation. MissARF employs adversarial random forest (ARF) for density estimation and data synthesis. To impute a missing value of an observation, we condition on the non-missing values and sample from the estimated conditional distribution generated by ARF. Our experiments demonstrate that MissARF performs comparably to state-of-the-art single and multiple imputation methods in terms of imputation quality and fast runtime with no additional costs for multiple imputation. |
| title | Missing value imputation with adversarial random forests -- MissARF |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15681 |