{"ID":2829874,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.11593","arxiv_id":"2512.11593","title":"Neural Network-based Partial-Linear Single-Index Models for Environmental Mixtures Analysis","abstract":"Evaluating the health effects of complex environmental mixtures remains a central challenge in environmental health research. Existing approaches vary in their flexibility, interpretability, scalability, and support for diverse outcome types, often limiting their utility in real-world applications. To address these limitations, we propose a neural network-based partial-linear single-index (NeuralPLSI) modeling framework that bridges semiparametric regression modeling interpretability with the expressive power of deep learning. The NeuralPLSI model constructs an interpretable exposure index via a learnable projection and models its relationship with the outcome through a flexible neural network. The framework accommodates continuous, binary, and time-to-event outcomes, and supports inference through a bootstrap-based procedure that yields confidence intervals for key model parameters. We evaluated NeuralPLSI through simulation studies under a range of scenarios and applied it to data from the National Health and Nutrition Examination Survey (NHANES) to demonstrate its practical utility. Together, our contributions establish NeuralPLSI as a scalable, interpretable, and versatile modeling tool for mixture analysis. To promote adoption and reproducibility, we release a user-friendly open-source software package that implements the proposed methodology and supports downstream visualization and inference (\\texttt{https://github.com/hyungrok-do/NeuralPLSI}).","short_abstract":"Evaluating the health effects of complex environmental mixtures remains a central challenge in environmental health research. Existing approaches vary in their flexibility, interpretability, scalability, and support for diverse outcome types, often limiting their utility in real-world applications. To address these lim...","url_abs":"https://arxiv.org/abs/2512.11593","url_pdf":"https://arxiv.org/pdf/2512.11593v1","authors":"[\"Hyungrok Do\",\"Yuyan Wang\",\"Mengling Liu\",\"Myeonggyun Lee\"]","published":"2025-12-12T14:28:47Z","proceeding":"stat.AP","tasks":"[\"stat.AP\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":605978,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2829874,"paper_url":"https://arxiv.org/abs/2512.11593","paper_title":"Neural Network-based Partial-Linear Single-Index Models for Environmental Mixtures Analysis","repo_url":"https://github.com/hyungrok-do/NeuralPLSI","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}