Bayesian variable selection for understanding mixtures in environmental exposures-Reference-Cited by-同舟云学术

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Bayesian variable selection for understanding mixtures in environmental exposures

Published:2021-06-15 Issue:22 Volume:40 Page:4850-4871
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statistics in Medicine

Author:

Kowal Daniel R.¹^ORCID,Bravo Mercedes²³,Leong Henry³,Bui Alexander⁴,Griffin Robert J.⁴,Ensor Katherine B.¹^ORCID,Miranda Marie Lynn³⁵

Affiliation:

1. Department of Statistics Rice University Houston Texas USA

2. Biostatistics and Epidemiology Division RTI International Durham North Carolina USA

3. Children's Environmental Health Initiative University of Notre Dame Notre Dame Indiana USA

4. Department of Civil and Environmental Engineering Rice University Houston Texas USA

5. Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre Dame Indiana USA

Funder

Army Research Office

National Institute of Environmental Health Sciences

National Institute on Minority Health and Health Disparities

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9099

Reference58 articles.

1. Racial residential segregation: A fundamental cause of racial disparities in health

2. The Environmental “Riskscape” and Social Inequality: Implicationsfor Explaining Maternal and Child Health Disparities

3. Air Pollution and Neuropsychological Development: A Review of the Latest Evidence

4. Prenatal particulate air pollution and neurodevelopment in urban children: Examining sensitive windows and sex-specific associations

5. Intellectual Impairment in Children with Blood Lead Concentrations below 10 μg per Deciliter

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spatial Variability in Relationships between Early Childhood Lead Exposure and Standardized Test Scores in Fourth Grade North Carolina Public School Students (2013–2016);Environmental Health Perspectives;2024-09

2. Combined Exposures and Mixtures Research: An Enduring NIEHS Priority;Environmental Health Perspectives;2024-07

3. Bayesian adaptive and interpretable functional regression for exposure profiles;The Annals of Applied Statistics;2024-03-01

4. Regression with race-modifiers: towards equity and interpretability;2024-01-04

5. Machine Learning Assisted Discovery of Interactions between Pesticides, Phthalates, Phenols, and Trace Elements in Child Neurodevelopment;Environmental Science & Technology;2023-08-18

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