Jackknife Model Averaging for Quantile Single-Index Coefficient Model  

作　　者：SUN Xianwen ZHANG Lixin 

机构地区：[1]School of Mathematical Sciences,Zhejiang University,Hangzhou 310058,China [2]School of Statistics and Mathematics,Zhejiang Gongshang University,Hangzhou 310018,China

出　　处：《Journal of Systems Science & Complexity》2024年第4期1685-1713,共29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.U23A2064 and 12031005。

摘　　要：In the past two decades,model averaging,as a way to solve model uncertainty,has attracted more and more attention.In this paper,the authors propose a jackknife model averaging(JMA) method for the quantile single-index coefficient model,which is widely used in statistics.Under model misspecification,the model averaging estimator is proved to be asymptotically optimal in terms of minimizing out-of-sample quantile loss.Simulation experiments are conducted to compare the JMA method with several model selections and model averaging methods,and the results show that the proposed method has a satisfactory performance.The method is also applied to a real dataset.

关 键 词：Final prediction error JMA criterion model averaging quantile loss single-index coefficient model 

分 类 号：O212.1[理学—概率论与数理统计]