Partial Linear Model Averaging Prediction for Longitudinal Data  

作　　者：LI Na FEI Yu ZHANG Xinyu 

机构地区：[1]Faculty of Science,Kunming University of Science and Technology,Kunming 650500,China [2]School of Statistics and Mathematics,Yunnan University of Finance and Economics,Kunming 650221,China [3]Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China

出　　处：《Journal of Systems Science & Complexity》2024年第2期863-885,共23页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.11971421,71925007,72091212,and 12288201;Yunling Scholar Research Fund of Yunnan Province under Grant No.YNWR-YLXZ-2018-020;the CAS Project for Young Scientists in Basic Research under Grant No.YSBR-008;the Start-Up Grant from Kunming University of Science and Technology under Grant No.KKZ3202207024.

摘　　要：Prediction plays an important role in data analysis.Model averaging method generally provides better prediction than using any of its components.Even though model averaging has been extensively investigated under independent errors,few authors have considered model averaging for semiparametric models with correlated errors.In this paper,the authors offer an optimal model averaging method to improve the prediction in partially linear model for longitudinal data.The model averaging weights are obtained by minimizing criterion,which is an unbiased estimator of the expected in-sample squared error loss plus a constant.Asymptotic properties,including asymptotic optimality and consistency of averaging weights,are established under two scenarios:(i)All candidate models are misspecified;(ii)Correct models are available in the candidate set.Simulation studies and an empirical example show that the promise of the proposed procedure over other competitive methods.

关 键 词：Asymptotic optimality longitudinal data model averaging estimator partially linear model PREDICTION 

分 类 号：O212[理学—概率论与数理统计]