A Novel Principal Component Analysis for Spatially Misaligned Multivariate Air Pollution Data
提出一种预测性稀疏主成分分析方法，处理空间错位数据，使主成分得分可在无监测点位置通过空间统计预测，用于识别空气污染物混合物并评估其健康效应。
We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.