Jackknife Model Averaging for Composite Quantile Regression  

作　　者：YOU Kang WANG Miaomiao ZOU Guohua 

机构地区：[1]School of Mathematical Sciences,Capital Normal University,Beijing 100089,China [2]School of Chinese Materia Medica,Beijing University of Chinese Medicine,Beijing 100105,China

出　　处：《Journal of Systems Science & Complexity》2024年第4期1604-1637,共34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.11971323 and 12031016。

摘　　要：In this paper,the authors propose a frequentist model averaging method for composite quantile regression with diverging number of parameters.Different from the traditional model averaging for quantile regression which considers only a single quantile,the proposed model averaging estimator is based on multiple quantiles.The well-known delete-one cross-validation or jackknife approach is applied to estimate the model weights.The resultant jackknife model averaging estimator is shown to be asymptotically optimal in terms of minimizing the out-of-sample composite final prediction error.Simulation studies are conducted to demonstrate the finite sample performance of the new model averaging estimator.The proposed method is also applied to the analysis of the stock returns data and the wage data.

关 键 词：Asymptotic optimality composite quantile regression cross-validation model averaging 

分 类 号：O212.1[理学—概率论与数理统计]