Critical window variable selection for mixtures: estimating the impact of multiple air pollutants on stillbirth

DOI10.1214/21-AOAS1560zbMath1498.62267arXiv2104.09730OpenAlexW3152574996MaRDI QIDQ2170420

Joshua L. Warren, Lauren K. Warren, Howard H. Chang, James A. Mulholland, Matthew J. Strickland, Lyndsey A. Darrow

Publication date: 5 September 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2104.09730

zbMATH Keywords

Bayesian variable selectioncorrelated weightsmultivariate latent variablespollution mixturesreproductive health

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Related Items (1)

CWVSmix

Uses Software

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Cites Work

• Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting
• Optimal predictive model selection.
• Identifying main effects and interactions among exposures using Gaussian processes
• Integrative statistical methods for exposure mixtures and health
• Kernel machine and distributed lag models for assessing windows of susceptibility to environmental mixtures in children's health studies
• Estimating the health effects of environmental mixtures using Bayesian semiparametric regression and sparsity inducing priors
• Spatial‐Temporal Modeling of the Association between Air Pollution Exposure and Preterm Birth: Identifying Critical Windows of Exposure
• Bayesian Factor Analysis for Inference on Interactions

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