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Least Squares Model Averaging Based on Generalized Cross Validation

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Li Xinmin 1   Zou Guohua 2   Zhang Xinyu 3,4 *   Zhao Shangwei 5  
文摘 Frequentist model averaging has received much attention from econometricians and statisticians in recent years. A key problem with frequentist model average estimators is the choice of weights. This paper develops a new approach of choosing weights based on an approximation of generalized cross validation. The resultant least squares model average estimators are proved to be asymptotically optimal in the sense of achieving the lowest possible squared errors. Especially, the optimality is built under both discrete and continuous weigh sets. Compared with the existing approach based on Mallows criterion, the conditions required for the asymptotic optimality of the proposed method are more reasonable. Simulation studies and real data application show good performance of the proposed estimators.
来源 Acta Mathematicae Applicatae Sinica-English Series ,2021,37(3):495-509 【核心库】
DOI 10.1007/s10255-021-1024-x
关键词 asymptotic optimality ; frequentist model averaging ; generalized cross validation ; mallows criterion
地址

1. School of Mathematics and Statistics,Qingdao University, Qingdao, 266071  

2. School of Mathematical Sciences,Capital Normal University, Beijing, 100048  

3. Academy of Mathematics and Systems Science,Chinese Academy of Sciences, Beijing, 100190  

4. Beijing Academy of Artificial Intelligence, Beijing, 100084  

5. College of Science,Minzu University of China, Beijing, 100081

语种 英文
文献类型 研究性论文
ISSN 0168-9673
学科 数学
基金 supported by National Key R&D Program of China ;  国家科技部项目 ;  国家自然科学基金 ;  a joint grant from the Academy for Multidisciplinary Studies, Capital Normal University
文献收藏号 CSCD:7013765

参考文献 共 39 共2页

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