Cross-Validation Model Averaging for Generalized Functional Linear Model

被引:9
|
作者
Zhang, Haili [1 ,2 ]
Zou, Guohua [3 ]
机构
[1] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[2] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
[3] Capital Normal Univ, Sch Math Sci, Beijing 100048, Peoples R China
基金
中国国家自然科学基金;
关键词
generalized functional linear model; cross-validation; model averaging; asymptotic optimality; FOCUSED INFORMATION CRITERION; CENTRAL LIMIT-THEOREM; ASYMPTOTIC OPTIMALITY; SELECTION; CONSISTENCY; REGRESSION; CL;
D O I
10.3390/econometrics8010007
中图分类号
F [经济];
学科分类号
02 ;
摘要
Functional data is a common and important type in econometrics and has been easier and easier to collect in the big data era. To improve estimation accuracy and reduce forecast risks with functional data, in this paper, we propose a novel cross-validation model averaging method for generalized functional linear model where the scalar response variable is related to a random function predictor by a link function. We establish asymptotic theoretical result on the optimality of the weights selected by our method when the true model is not in the candidate model set. Our simulations show that the proposed method often performs better than the commonly used model selection and averaging methods. We also apply the proposed method to Beijing second-hand house price data.
引用
收藏
页数:35
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