Generalized Functional Linear Models: Efficient Modeling for High-dimensional Correlated Mixture Exposures.
GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Authors
- Zhuan Zhou (ORCID: https://orcid.org/0000-0001-9048-6638)
- Haiya Yan
- Yuehua Hu (ORCID: https://orcid.org/0000-0003-3027-1410)
- Xin Peng (ORCID: https://orcid.org/0000-0001-9277-8415)
- Yue Kuang (ORCID: https://orcid.org/0000-0003-0067-1915)
- Shunhong Luo (ORCID: https://orcid.org/0009-0000-7852-1488)
- Ou Chen (ORCID: https://orcid.org/0000-0003-2001-3565)
- Hai Bin Yu
- Bing Zhang (ORCID: https://orcid.org/0000-0003-0164-4193)
- Yi Zheng (ORCID: https://orcid.org/0009-0006-9660-1004)
- Lin Hua Liu
- Jin Ni
- Shuoyang Li
- Hui Zi WeiRen
Institutions
- Guangdong Medical College (CN)
- Chinese Center For Disease Control and Prevention (CN)
Publication Details
- Journal
- PubMed
- Published
- 2025-08-20
- DOI
- https://doi.org/10.3967/bes2025.024
- Primary Topic
- Statistical Methods and Inference
- Type
- article
- Field-Weighted Citation Impact
- 0.00