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| Main Authors: | , , |
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| Format: | Artículo Open Access |
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Wiley
2025
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| Online Access: | https://onlinelibrary.wiley.com/doi/10.1002/sim.70002 |
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| _version_ | 1867004549427888129 |
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| author | Zhanfeng Wang Jingyu Huang Yuan‐chin Ivan Chang |
| author_facet | Zhanfeng Wang Jingyu Huang Yuan‐chin Ivan Chang Zhanfeng Wang Jingyu Huang Yuan‐chin Ivan Chang |
| collection | Wiley Open Access |
| contents | Ensemble of Sequential Learning Models With Distributed Data Centers and Its Applications Zhanfeng Wang Jingyu Huang Yuan‐chin Ivan Chang Statistics in Medicine ABSTRACTHandling massive datasets poses a significant challenge in modern data analysis, particularly within epidemiology and medicine. In this study, we introduce a novel approach using sequential ensemble learning to effectively analyze extensive datasets. Our method prioritizes efficiency from both statistical and computational perspectives, addressing challenges such as data communication and privacy, as discussed in federated learning literature. To demonstrate the efficacy of our approach, we present compelling real‐world examples using COVID‐19 data alongside simulation studies. 10.1002/sim.70002 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.70002 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70002 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Ensemble of Sequential Learning Models With Distributed Data Centers and Its Applications Zhanfeng Wang Jingyu Huang Yuan‐chin Ivan Chang Statistics in Medicine Ensemble of Sequential Learning Models With Distributed Data Centers and Its Applications Zhanfeng Wang Jingyu Huang Yuan‐chin Ivan Chang Statistics in Medicine ABSTRACTHandling massive datasets poses a significant challenge in modern data analysis, particularly within epidemiology and medicine. In this study, we introduce a novel approach using sequential ensemble learning to effectively analyze extensive datasets. Our method prioritizes efficiency from both statistical and computational perspectives, addressing challenges such as data communication and privacy, as discussed in federated learning literature. To demonstrate the efficacy of our approach, we present compelling real‐world examples using COVID‐19 data alongside simulation studies. 10.1002/sim.70002 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Ensemble of Sequential Learning Models With Distributed Data Centers and Its Applications |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70002 |