Communication-efficient estimation and inference for high-dimensional longitudinal data

With the rapid growth in modern science and technology, distributed longitudinal data have drawn attention in a wide range of aspects. Realizing that not all effects of covariates are our parameters of interest, we focus on the distributed estimation and statistical inference of a pre-conceived low-dimensional parameter in the high-dimensional longitudinal GLMs with canonical links. To mitigate the impact of high-dimensional nuisance parameters and incorporate the within-subject correlation simultaneously, a decorrelated quadratic inference function is proposed for enhancing the estimation efficiency. Two communication-efficient surrogate decorrelated score estimators based on multi-round iterative algorithms are proposed. The error bounds and limiting distribution of the proposed estimators are established and extensive numerical experiments demonstrate the effectiveness of our method. An application to the National Longitudinal Survey of Youth Dataset is also presented.