Bayesian adaptive and interpretable functional regression for exposure profiles
Yunan Gao, Daniel R. Kowal

TL;DR
This paper introduces a Bayesian functional regression model with adaptive shrinkage for analyzing the impact of prenatal air pollution exposure on children's educational outcomes, identifying critical susceptibility windows.
Contribution
It develops a scalable Bayesian model with dynamic shrinkage priors and a novel decision analysis for interpretability in functional regression.
Findings
Early and late pregnancy PM2.5 exposure adversely affects reading scores.
The model outperforms existing methods in window detection and uncertainty quantification.
Simulation studies validate improved accuracy and interpretability.
Abstract
Pollutant exposure during gestation is a known and adverse factor for birth and health outcomes. However, the links between prenatal air pollution exposures and educational outcomes are less clear, in particular the critical windows of susceptibility during pregnancy. Using a large cohort of students in North Carolina, we study the link between prenatal daily exposure and 4th end-of-grade reading scores. We develop and apply a locally adaptive and highly scalable Bayesian regression model for scalar responses with functional and scalar predictors. The proposed model pairs a B-spline basis expansion with dynamic shrinkage priors to capture both smooth and rapidly-changing features in the regression surface. The model is accompanied by a new decision analysis approach for functional regression that extracts the critical windows of susceptibility and guides the model…
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Taxonomy
TopicsAir Quality and Health Impacts · Energy and Environment Impacts · Air Quality Monitoring and Forecasting
