Average Estimation of Semiparametric Models for High-Dimensional Longitudinal Data

Model average receives much attention in recent years. This paper considers the semiparametric model averaging for high-dimensional longitudinal data. To minimize the prediction error, the authors estimate the model weights using a leave-subject-out cross-validation procedure. Asymptotic optimality of the proposed method is proved in the sense that leave-subject-out cross-validation achieves the lowest possible prediction loss asymptotically. Simulation studies show that the performance of the proposed model average method is much better than that of some commonly used model selection and averaging methods.