Model Averaging Estimation for Varying-Coefficient Single-Index Models  

作　　者：LIU Yue ZOU Jiahui ZHAO Shangwei YANG Qinglong 

机构地区：[1]School of Statistics,Jiangxi University of Finance and Economics,Nanchang 330013,China. [2]School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing 100049,China [3]Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China. [4]School of Science,Minzu University of China,Beijing 100081,China [5]School of Statistics and Mathematics,Zhongnan University of Economics and Law,Wuhan 430073,China

出　　处：《Journal of Systems Science & Complexity》2022年第1期264-282,共19页系统科学与复杂性学报（英文版）

基　　金：supported by the National Nature Science Foundation of China;under Grant Nos.12001559and 11971324;the Ministry of Education of Humanities and Social Science project;under Grant No.19YJC910008。

摘　　要：The varying-coefficient single-index model(VCSIM)is widely used in economics,statistics and biology.A model averaging method for VCSIM based on a Mallows-type criterion is proposed to improve prodictive capacity,which allows the number of candidate models to diverge with sample size.Under model misspecification,the asymptotic optimality is derived in the sense of achieving the lowest possible squared errors.The authors compare the proposed model averaging method with several other classical model selection methods by simulations and the corresponding results show that the model averaging estimation has a outstanding performance.The authors also apply the method to a real dataset.

关 键 词：Asymptotic optimality kernel-local smoothing method Mallows-type criterion model averaging varying-coefficient single-index model 

分 类 号：O21[理学—概率论与数理统计]