论文标题
贝叶斯自适应和可解释的功能回归，用于暴露概况
Bayesian adaptive and interpretable functional regression for exposure profiles
论文作者
论文摘要
妊娠期间的污染物暴露是出生和健康结果的已知和不利因素。但是，产前空气污染暴露与教育结果之间的联系还不太清楚，尤其是怀孕期间易感性的关键窗口。我们使用北卡罗来纳州的一大批学生，我们研究产前每日$ \ mbox {pm} _ {2.5} $曝光与第四年级阅读分数之间的联系。我们为具有功能和标量预测指标的标量响应开发并应用了局部自适应和高度可扩展的贝叶斯回归模型。提出的模型与动态收缩先验配对A B型基碱基扩展，以捕获回归表面中的平滑和快速变化的特征。该模型伴随着一种用于功能回归的新决策分析方法，该方法提取了易感性的关键窗口并指导模型解释。这些工具有助于通过功能回归模型的解释性来识别和解决广泛的局限性。模拟研究表明，比现有方法更准确的点估计，更精确的不确定性量化和窗口选择要出色。利用所提出的建模，计算和决策分析框架，我们得出结论，在怀孕早期和晚期，产前$ \ mbox {pm} _ {2.5} $暴露是第四年级阅读量的最不利的。
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 $\mbox{PM}_{2.5}$ 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 interpretations. These tools help to identify and address broad limitations with the interpretability of functional regression models. Simulation studies demonstrate more accurate point estimation, more precise uncertainty quantification, and far superior window selection than existing approaches. Leveraging the proposed modeling, computational, and decision analysis framework, we conclude that prenatal $\mbox{PM}_{2.5}$ exposure during early and late pregnancy is most adverse for 4th end-of-grade reading scores.
