首页|Partial Linear Model Averaging Prediction for Longitudinal Data

Partial Linear Model Averaging Prediction for Longitudinal Data

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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.

Asymptotic optimalitylongitudinal datamodel averaging estimatorpartially linear modelprediction

LI Na、FEI Yu、ZHANG Xinyu

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Faculty of Science,Kunming University of Science and Technology,Kunming 650500,China

School of Statistics and Mathematics,Yunnan University of Finance and Economics,Kunming 650221,China

Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金Yunling Scholar Research Fund of Yunnan ProvinceCAS Project for Young Scientists in Basic ResearchStart-Up Grant from Kunming University of Science and Technology

11971421719250077209121212288201YNWR-YLXZ-2018-020YSBR-008KKZ3202207024

2024

10.1007/s11424-024-2187-3

系统科学与复杂性学报(英文版)
中国科学院系统科学研究所

系统科学与复杂性学报(英文版)

EI
影响因子：0.181
ISSN：1009-6124
年,卷(期)：2024.37(2)
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