Distributed subsampling and quasi decorrelated score for cluster data: An application to Beijing multisite air quality
Junzhuo Gao, Lei Wang, Jun Shao
The Annals of Applied Statistics · 2025 · Volume 19 · Issue 3
Abstract
Forecasting and controlling PM2.5 emissions is crucial for environmental protection and public health. To analyze the Beijing multisite air quality dataset on regional and seasonal effects in PM2.5 emissions, which has large-scale distributed cluster/longitudinal data and high-dimensional covariates, we develop a unified cluster subsampling method for generalized linear models (GLMs) to downsize the data volume and reduce computational burden. To incorporate the within-subject correlation, a weighted generalized estimation equation under an informative working correlation structure is considered, and a novel optimal subsampling criterion including both the A- and L-optimality is proposed. For low-dimensional GLMs, the resulting optimal subsample estimators are consistent and asymptotically normal with explicitly derived asymptotic covariance matrices. For the preconceived low-dimensional parameter in high-dimensional GLMs, a quasi decorrelated score function is developed to mitigate the effect from nuisance parameter estimation. Our proposed method is evaluated by simulation. By applying our method to the Beijing multisite air quality dataset, we reveal that the PM2.5 emissions in the south part of Beijing have a U-shaped seasonal effect in the order of winter, spring, summer, and autumn, and a regional aggregation effect in winter of southeastern Beijing.
Keywords
generalized linear models, high-dimensional nuisance parameter, optimal distributed cluster subsampling, quasi score and decorrelated score, within-subject correlation
Enable JavaScript for citation tools and additional paper interactions.