Mastering Rare Event Analysis: Optimal Subsample Size in Logistic and Cox Regressions
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866911926998532096 |
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| author | Agassi, Tal Keret, Nir Gorfine, Malka |
| author_facet | Agassi, Tal Keret, Nir Gorfine, Malka |
| contents | In the realm of contemporary data analysis, the use of massive datasets has taken on heightened significance, albeit often entailing considerable demands on computational time and memory. While a multitude of existing works offer optimal subsampling methods for conducting analyses on subsamples with minimized efficiency loss, they notably lack tools for judiciously selecting the optimal subsample size. To bridge this gap, our work introduces tools designed for choosing the optimal subsample size. We focus on three settings: the Cox regression model for survival data with rare events and logistic regression for both balanced and imbalanced datasets. Additionally, we present a novel optimal subsampling procedure tailored for logistic regression with imbalanced data. The efficacy of these tools and procedures is demonstrated through an extensive simulation study and meticulous analyses of two sizable datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13836 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mastering Rare Event Analysis: Optimal Subsample Size in Logistic and Cox Regressions Agassi, Tal Keret, Nir Gorfine, Malka Methodology In the realm of contemporary data analysis, the use of massive datasets has taken on heightened significance, albeit often entailing considerable demands on computational time and memory. While a multitude of existing works offer optimal subsampling methods for conducting analyses on subsamples with minimized efficiency loss, they notably lack tools for judiciously selecting the optimal subsample size. To bridge this gap, our work introduces tools designed for choosing the optimal subsample size. We focus on three settings: the Cox regression model for survival data with rare events and logistic regression for both balanced and imbalanced datasets. Additionally, we present a novel optimal subsampling procedure tailored for logistic regression with imbalanced data. The efficacy of these tools and procedures is demonstrated through an extensive simulation study and meticulous analyses of two sizable datasets. |
| title | Mastering Rare Event Analysis: Optimal Subsample Size in Logistic and Cox Regressions |
| topic | Methodology |
| url | https://arxiv.org/abs/2406.13836 |