Probabilistic predictive principal component analysis for spatially misaligned and high-dimensional air pollution data with missing observations

Accurate predictions of pollutant concentrations at new locations are often of interest in air pollution studies on fine particulate matters (PM2.5), in which data are usually not measured at all study locations. PM2.5 is also a mixture of many different chemical components. Principal component analysis (PCA) can be incorporated to obtain lowerdimensional representative scores of such multipollutant data. Spatial prediction can then be used to estimate these scores at new locations. Recently developed predictive PCA modifies the traditional PCA algorithm to obtain scores with spatial structures that can be well predicted at unmeasured locations. However, these approaches require complete data, whereas multipollutant data tend to have complex missing patterns in practice. We propose probabilistic versions of predictive PCA, which allow for flexible model-based imputation that can account for spatial information and subsequently improve the overall predictive performance.