Adaptive predictive principal components for modeling multivariate air pollution.

ABSTRACT: Air pollution monitoring locations are typically spatially misaligned with locations of participants in a cohort study, so to analyze pollution-health associations, exposures must be predicted at subject locations. For a pollution measure like PM_2.5 (fine particulate matter) comprised of multiple chemical components, the predictive principal component analysis (PCA) algorithm derives a low-dimensional representation of component profiles for use in health analyses. Geographic covariates and spatial splines help determine the principal component loadings of the pollution data to give improved prediction accuracy of the principal component scores. While predictive PCA can accommodate pollution data of arbitrary dimension, it is currently limited to a small number of pre-selected g

SUBMITTER: Bose M 

PROVIDER: S-EPMC7313718 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature