Longitudinal data analysis using sufficient dimension reduction method

There have been an increasing number of applications where the number of predictors is large, meanwhile data are repeatedly measured at a sequence of time points. In this article we investigate how dimension reduction method can be employed for analyzing such high-dimensional longitudinal data. Predictor dimension can be effectively reduced while full regression means information can be retained during dimension reduction. Simultaneous variable selection along with dimension reduction is studied, and graphical diagnosis and model fitting after dimension reduction are investigated. The method is flexible enough to encompass a variety of commonly used longitudinal models.