Average Estimation of Semiparametric Models for High-Dimensional Longitudinal Data  

作　　者：ZHAO Zhihao ZOU Guohua 

机构地区：[1]School of Mathematicnl Sciences,Capital Normal University,Beijing 100048,China

出　　处：《Journal of Systems Science & Complexity》2020年第6期2013-2047,共35页系统科学与复杂性学报（英文版）

基　　金：the Ministry of Science and Technology of China under Grant No.2016YFB0502301;Academy for Multidisciplinary Studies of Capital Normal University,and the National Natural Science Foundation of China under Grant Nos.11971323 and 11529101。

摘　　要：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.

关 键 词：Asymptotic optimality high-dimensional longitudinal data leave-subject-out cross-validation model averaging semiparametric models 

分 类 号：O212.1[理学—概率论与数理统计]