Model-averaging-based semiparametric modeling for conditional quantile prediction  

作　　者：Chaohui Guo Wenyang Zhang 

机构地区：[1]School of Mathematical Sciences,Chongqing Normal University,Chongqing 401331,China [2]Department of Mathematics,University of York,York YO105DD,UK

出　　处：《Science China Mathematics》2024年第12期2843-2872,共30页中国科学（数学英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.11931014 and 12201091);the Natural Science Foundation of Chongqing(Grant No.CSTB2022NSCQ-MSX0852);the National Statistical Science Research Program of China(Grant No.2022LY019);the Science and Technology Research Program of the Chongqing Municipal Education Commission(Grant No.KJQN202100526)。

摘　　要：In real data analysis,the underlying model is frequently unknown.Hence,the modeling strategy plays a key role in the success of data analysis.Inspired by the idea of model averaging,we propose a novel semiparametric modeling strategy for the conditional quantile prediction,without assuming that the underlying model is any specific parametric or semiparametric model.Due to the optimality of the weights selected by leaveone-out cross-validation,the proposed modeling strategy provides a more precise prediction than those based on some commonly used semiparametric models such as the varying coefficient and additive models.Asymptotic properties are established in the proposed modeling strategy along with its estimation procedure.We conducted extensive simulations to compare our method with alternatives across various scenarios.The results show that our method provides more accurate predictions.Finally,we applied our approach to the Boston housing data,yielding more precise quantile predictions of house prices compared with commonly used methods,and thus offering a clearer picture of the Boston housing market.

关 键 词：asymptotic optimality conditional quantile prediction kernel smoothing leave-one-out crossvalidation model averaging varying coefficient model 

分 类 号：O212[理学—概率论与数理统计]