Estimation in functional single-index varying coefficient model

被引:11
作者
Feng, Sanying [1 ]
Tian, Ping [2 ]
Hu, Yuping [1 ]
Li, Gaorong [3 ]
机构
[1] Zhengzhou Univ, Sch Math & Stat, Zhengzhou 450001, Peoples R China
[2] Xuchang Univ, Sch Math & Stat, Xuchang 461000, Peoples R China
[3] Beijing Normal Univ, Sch Stat, Beijing 100875, Peoples R China
基金
中国国家自然科学基金;
关键词
B splines; Convergence rate; Functional principal components; Functional single-index varying coefficient model; Mean squared prediction error;
D O I
10.1016/j.jspi.2021.01.003
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Functional regression allows for a scalar response to be dependent on a functional predictor, however, not much work has been done when scalar predictors that interacts with the functional predictor are introduced. In this paper, we introduce a new functional single-index varying coefficient model with the functional predictor being single-index part. By means of functional principal components analysis and basis function approximation, we obtain the estimators of slope function and coefficient functions, and propose an iterative estimating procedure. Furthermore, the rates of convergence of the proposed estimators and the mean squared prediction error are established under some regularity conditions. At last, we illustrate the finite sample performance of our proposed methods with some simulation studies and a real data application. (C) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页码:62 / 75
页数:14
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