LAUSR

This paper is concerned with feature screening for the ultrahigh dimensional additive models with longitudinal data. The proposed method utilizes the quadratic inference functions to construct the marginal screening measurement, which takes the within-subject correlation into consideration and is more efficient and robust than some parametric model assumptions for the working covariance matrix in each subject or experimental unit. We also show that the proposed method enjoys the sure screening property under some regularity conditions. Monte Carlo simulation studies and a real data application are conducted to examine the performance of the proposed method.