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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.03665 |
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| _version_ | 1866914496730103808 |
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| author | Sverdrup, Erik Yang, James LeBlanc, Michael |
| author_facet | Sverdrup, Erik Yang, James LeBlanc, Michael |
| contents | Random survival forests are widely used for estimating covariate-conditional survival functions under right-censoring. Their standard log-rank splitting criterion is typically recomputed at each candidate split. This O(M) cost per split, with M the number of distinct event times in a node, creates a bottleneck for large cohort datasets with long follow-up. We revisit approximations proposed by LeBlanc and Crowley (1995) and develop simple constant-time updates for the log-rank criterion. The method is implemented in grf for R and reduces training time on large datasets while preserving predictive accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Log-Rank Updates for Random Survival Forests Sverdrup, Erik Yang, James LeBlanc, Michael Methodology Computation Random survival forests are widely used for estimating covariate-conditional survival functions under right-censoring. Their standard log-rank splitting criterion is typically recomputed at each candidate split. This O(M) cost per split, with M the number of distinct event times in a node, creates a bottleneck for large cohort datasets with long follow-up. We revisit approximations proposed by LeBlanc and Crowley (1995) and develop simple constant-time updates for the log-rank criterion. The method is implemented in grf for R and reduces training time on large datasets while preserving predictive accuracy. |
| title | Efficient Log-Rank Updates for Random Survival Forests |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2510.03665 |